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2020

  • Content Planning for Neural Story Generation with Aristotelian Rescoring

    Seraphina Goldfarb-Tarrant, Tuhin Chakrabarty, Ralph Weischedel, and Nanyun Peng, in the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
    Full Text Slides Code Abstract BibTeX Details
    Long-form narrative text generated from largelanguage models manages a fluent impersonation of human writing, but only at the localsentence level, and lacks structure or global cohesion. We posit that many of the problem of story generation can be addressed via high quality content planning, and present a systemthat focuses on how to learn good plot structures to guide story generation. We utilize a plot-generation language model along with an ensemble of rescoring models that each implement an aspect of good story-writing as detailed in Aristotle’s Poetics. We find that stories written with our more principled plot structure are both more relevant to a given prompt and higher quality than baselines that do not content plan, or that plan in an unprincipled way.
    @inproceedings{goldfarb2020content,
      title = {Content Planning for Neural Story Generation with Aristotelian Rescoring},
      author = {Goldfarb-Tarrant, Seraphina and Chakrabarty, Tuhin and Weischedel, Ralph and Peng, Nanyun},
      booktitle = {the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
      pages = {4319--4338},
      slideslive_id = {38939240},
      year = {2020}
    }
    
    Details
  • Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation

    Tuhin Chakrabarty, Smaranda Muresan, and Nanyun Peng, in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
    Full Text Slides Code Abstract BibTeX Details
    Literary tropes, from poetry to stories, are at the crux of human imagination and communication. Figurative language, such as a simile,goes beyond plain expressions to give readers new insights and inspirations. We tackle the problem of simile generation. Generating a simile requires proper understanding for effective mapping of properties between two concepts. To this end, we first propose a method to automatically construct a parallel corpus by transforming a large number of similes collected from Reddit to their literal counterpart using structured common sense knowledge. We then fine-tune a pretrained sequence to sequence model, BART (Lewis et al., 2019),on the literal-simile pairs to generate novel similes given a literal sentence. Experiments show that our approach generates 88% novel similes that do not share properties with the training data. Human evaluation on an independent set of literal statements shows that our model generates similes better than two literary experts 37% of the times, and three baseline systems including a recent metaphor generation model 71% of the times when compared pairwise. We also show how replacing literal sentences with similes from our best model in machine generated stories improves evocativeness and leads to better acceptance by human judges.
    @inproceedings{chakrabarty-etal-2020-generating,
      title = {Generating similes effortlessly like a Pro: A Style Transfer Approach for Simile Generation},
      author = {Chakrabarty, Tuhin and Muresan, Smaranda and Peng, Nanyun},
      booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
      pages = {6455--6469},
      publisher = {Association for Computational Linguistics},
      slideslive_id = {38938962},
      year = {2020}
    }
    
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  • Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction

    Rujun Han, Yichao Zhou, and Nanyun Peng, in the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
    Full Text Slides Code Abstract BibTeX Details
    Extracting event temporal relations is a critical task for information extraction and plays an important role in natural language understanding. Prior systems leverage deep learning and pre-trained language models to improve the performance of the task. However, these systems often suffer from two shortcomings: 1) when performing maximum a posteriori (MAP) inference based on neural models, previous systems only used structured knowledge that is assumed to be absolutely correct, i.e., hard constraints; 2) biased predictions on dominant temporal relations when training with a limited amount of data. To address these issues, we propose a framework that enhances deep neural network with distributional constraints constructed by probabilistic domain knowledge. We solve the constrained inference problem via Lagrangian Relaxation and apply it to end-to-end event temporal relation extraction tasks. Experimental results show our framework is able to improve the baseline neural network models with strong statistical significance on two widely used datasets in news and clinical domains.
    @inproceedings{han2020knowledge,
      title = {Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction},
      author = {Han, Rujun and Zhou, Yichao and Peng, Nanyun},
      booktitle = {the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
      publisher = {Association for Computational Linguistics},
      pages = {5717--5729},
      slideslive_id = {38939236},
      year = {2020}
    }
    
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  • TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions

    Qiang Ning, Hao Wu, Rujun Han, Nanyun Peng, Matt Gardner, and Dan Roth, in the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
    Full Text Code Abstract BibTeX Details
    A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated. However, current machine reading comprehension benchmarks have practically no questions that test temporal phenomena, so systems trained on these benchmarks have no capacity to answer questions such as "what happened before/after [some event]?" We introduce TORQUE, a new English reading comprehension benchmark built on 3.2k news snippets with 21k human-generated questions querying temporal relationships. Results show that RoBERTa-large achieves an exact-match score of 51% on the test set of TORQUE, about 30% behind human performance.
    @inproceedings{ning2020torque,
      title = {TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions},
      author = {Ning, Qiang and Wu, Hao and Han, Rujun and Peng, Nanyun and Gardner, Matt and Roth, Dan},
      booktitle = {the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
      publisher = {Association for Computational Linguistics},
      pages = {1158--1172},
      slideslive_id = {38938807},
      year = {2020}
    }
    
    Details
  • STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation

    Nader Akoury, Shufan Wang, Josh Whiting, Stephen Hood, Nanyun Peng, and Mohit Iyyer, in the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020.
    Full Text Code Abstract BibTeX Details
    Systems for story generation are asked to produce plausible and enjoyable stories given an input context. This task is underspecified, as a vast number of diverse stories can originate from a single input. The large output space makes it difficult to build and evaluate story generation models, as (1) existing datasets lack rich enough contexts to meaningfully guide models, and (2) existing evaluations (both crowdsourced and automatic) are unreliable for assessing long-form creative text. To address these issues, we introduce a dataset and evaluation platform built from STORIUM, an online collaborative storytelling community. Our author-generated dataset contains 6K lengthy stories (125M tokens) with fine-grained natural language annotations (e.g., character goals and attributes) interspersed throughout each narrative, forming a robust source for guiding models. We evaluate language models fine-tuned on our dataset by integrating them onto STORIUM, where real authors can query a model for suggested story continuations and then edit them. Automatic metrics computed over these edits correlate well with both user ratings of generated stories and qualitative feedback from semi-structured user interviews. We release both the STORIUM dataset and evaluation platform to spur more principled research into story generation.
    @inproceedings{akoury2020storium,
      title = {STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation},
      author = {Akoury, Nader and Wang, Shufan and Whiting, Josh and Hood, Stephen and Peng, Nanyun and Iyyer, Mohit},
      booktitle = {the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
      slideslive_id = {38939010},
      year = {2020}
    }
    
    Details
  • Towards Controllable Biases in Language Generation

    Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)-Findings, long, 2020.
    Full Text Poster Code Abstract BibTeX Details
    We present a general approach towards controllable societal biases in natural language generation (NLG). Building upon the idea of adversarial triggers, we develop a method to induce societal biases in generated text when input prompts contain mentions of specific demographic groups. We then analyze two scenarios: 1) inducing negative biases for one demographic and positive biases for another demographic, and 2) equalizing biases between demographics. The former scenario enables us to detect the types of biases present in the model. Specifically, we show the effectiveness of our approach at facilitating bias analysis by finding topics that correspond to demographic inequalities in generated text and comparing the relative effectiveness of inducing biases for different demographics. The second scenario is useful for mitigating biases in downstream applications such as dialogue generation. In our experiments, the mitigation technique proves to be effective at equalizing the amount of biases across demographics while simultaneously generating less negatively biased text overall.
    @inproceedings{sheng2020towards,
      title = {Towards Controllable Biases in Language Generation},
      author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
      booktitle = {the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)-Findings, long},
      year = {2020}
    }
    
    Details
  • Biomedical Event Extraction with Hierarchical Knowledge Graphs

    Kung-Hsiang Huang, Mu Yang, and Nanyun Peng, in the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)-Findings, short, 2020.
    Full Text Slides Code Abstract BibTeX Details
    Biomedical event extraction is critical in understanding biomolecular interactions described in scientific corpus. One of the main challenges is to identify nested structured events that are associated with non-indicative trigger words. We propose to incorporate domain knowledge from Unified Medical Language System (UMLS) to a pre-trained language model via a hierarchical graph representation encoded by a proposed Graph Edgeconditioned Attention Networks (GEANet). To better recognize the trigger words, each sentence is first grounded to a sentence graph based on a jointly modeled hierarchical knowledge graph from UMLS. The grounded graphs are then propagated by GEANet, a novel graph neural networks for enhanced capabilities in inferring complex events. On BioNLP 2011 GENIA Event Extraction task, our approach achieved 1.41% F1 and 3.19% F1 improvements on all events and complex events, respectively. Ablation studies confirm the importance of GEANet and hierarchical KG.
    @inproceedings{huang2020event,
      title = {Biomedical Event Extraction with Hierarchical Knowledge Graphs},
      author = {Huang, Kung-Hsiang and Yang, Mu and Peng, Nanyun},
      booktitle = {the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)-Findings, short},
      slideslive_id = {38940169},
      year = {2020}
    }
    
    Details
  • Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering

    Peifeng Wang, Nanyun Peng, Filip Ilievski, Pedro Szekely, and Xiang Ren, in the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)-Findings, 2020.
    Full Text Code Abstract BibTeX Details
    Commonsense question answering (QA) requires background knowledge which is not explicitly stated in a given context. Prior works use commonsense knowledge graphs (KGs) to obtain this knowledge for reasoning. However, relying entirely on these KGs may not suffice, considering their limited coverage and the contextual dependence of their knowledge. In this paper, we augment a general commonsense QA framework with a knowledgeable path generator. By extrapolating over existing paths in a KG with a state-of-the-art language model, our generator learns to connect a pair of entities in text with a dynamic, and potentially novel, multi-hop relational path. Such paths can provide structured evidence for solving commonsense questions without fine-tuning the path generator. Experiments on two datasets show the superiority of our method over previous works which fully rely on knowledge from KGs (with up to 6% improvement in accuracy), across various amounts of training data. Further evaluation suggests that the generated paths are typically interpretable, novel, and relevant to the task.
    @inproceedings{wang2020connecting,
      title = {Connecting the Dots: A Knowledgeable Path Generator for Commonsense Question Answering},
      author = {Wang, Peifeng and Peng, Nanyun and Ilievski, Filip and Szekely, Pedro and Ren, Xiang},
      booktitle = {the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)-Findings},
      pages = {4129--4140},
      year = {2020}
    }
    
    Details
  • R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge

    Tuhin Chakrabarty, Debanjan Ghosh, Smaranda Muresan, and Nanyun Peng, in the 2020 Annual Conference of the Association for Computational Linguistics (ACL), 2020.
    Full Text BibTeX Details
    @inproceedings{chakrabarty2020r,
      title = {R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge},
      author = {Chakrabarty, Tuhin and Ghosh, Debanjan and Muresan, Smaranda and Peng, Nanyun},
      booktitle = {the 2020 Annual Conference of the Association for Computational Linguistics (ACL)},
      year = {2020}
    }
    
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  • Predictive Engagement: An Efficient Metric For Automatic Evaluation of Open-Domain Dialogue Systems

    Sarik Ghazarian, Ralph Weischedel, Aram Galstyan, and Nanyun Peng, in The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), 2020.
    Full Text BibTeX Details
    @inproceedings{ghazarian2020predictive,
      title = {Predictive Engagement: An Efficient Metric For Automatic Evaluation of Open-Domain Dialogue Systems},
      author = {Ghazarian, Sarik and Weischedel, Ralph and Galstyan, Aram and Peng, Nanyun},
      booktitle = {The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20)},
      year = {2020}
    }
    
    Details
  • Enabling Low-Resource Transfer Learning across COVID-19 Corpora by Combining Event-Extraction and Co-Training

    Alexander Spangher, Nanyun Peng, Jonathan May, and Emilio Ferrara, in ACL 2020 Workshop on Natural Language Processing for COVID-19 (NLP-COVID), 2020.
    Full Text BibTeX Details
    @inproceedings{spangher2020enabling,
      title = {Enabling Low-Resource Transfer Learning across COVID-19 Corpora by Combining Event-Extraction and Co-Training},
      author = {Spangher, Alexander and Peng, Nanyun and May, Jonathan and Ferrara, Emilio},
      booktitle = {ACL 2020 Workshop on Natural Language Processing for COVID-19 (NLP-COVID)},
      year = {2020}
    }
    
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  • Man is to person as woman is to location: Measuring gender bias in named entity recognition

    Ninareh Mehrabi, Thamme Gowda, Fred Morstatter, Nanyun Peng, and Aram Galstyan, in 31st ACM Conference on Hypertext and Social Media (HT’20), 2020.
    Full Text BibTeX Details
    @inproceedings{mehrabi2020man,
      title = {Man is to person as woman is to location: Measuring gender bias in named entity recognition},
      author = {Mehrabi, Ninareh and Gowda, Thamme and Morstatter, Fred and Peng, Nanyun and Galstyan, Aram},
      booktitle = {31st ACM Conference on Hypertext and Social Media (HT’20)},
      year = {2020}
    }
    
    Details

2019

  • Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction

    Rujun Han, Qiang Ning, and Nanyun Peng, in 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
    Full Text BibTeX Details
    @inproceedings{han2019joint,
      title = {Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction},
      author = {Han, Rujun and Ning, Qiang and Peng, Nanyun},
      booktitle = {2019 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
      year = {2019}
    }
    
    Details
  • The Woman Worked as a Babysitter: On Biases in Language Generation

    Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng, in 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), short, 2019.
    Full Text BibTeX Details
    @inproceedings{sheng2019woman,
      title = {The Woman Worked as a Babysitter: On Biases in Language Generation},
      author = {Sheng, Emily and Chang, Kai-Wei and Natarajan, Premkumar and Peng, Nanyun},
      booktitle = {2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), short},
      year = {2019}
    }
    
    Details
  • Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing

    Tao Meng, Nanyun Peng, and Kai-Wei Chang, in 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019.
    Full Text BibTeX Details
    @inproceedings{meng2019target,
      title = {Target Language-Aware Constrained Inference for Cross-lingual Dependency Parsing},
      author = {Meng, Tao and Peng, Nanyun and Chang, Kai-Wei},
      booktitle = {2019 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
      year = {2019}
    }
    
    Details
  • What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis

    Xiaolei Huang, Jonathan May, and Nanyun Peng, in 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), short, 2019.
    Full Text BibTeX Details
    @inproceedings{huang2019matters,
      title = {What Matters for Neural Cross-Lingual Named Entity Recognition: An Empirical Analysis},
      author = {Huang, Xiaolei and May, Jonathan and Peng, Nanyun},
      booktitle = {2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), short},
      year = {2019}
    }
    
    Details
  • Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects

    James Mullenbach, Jonathan Gordon, Nanyun Peng, and Jonathan May, in Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), short, 2019.
    Full Text BibTeX Details
    @inproceedings{mullenbach2019nuclear,
      title = {Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects},
      author = {Mullenbach, James and Gordon, Jonathan and Peng, Nanyun and May, Jonathan},
      booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), short},
      pages = {6054--6060},
      year = {2019}
    }
    
    Details
  • Deep Structured Neural Network for Event Temporal Relation Extraction

    Rujun Han, I. Hsu, Mu Yang, Aram Galstyan, Ralph Weischedel, and Nanyun Peng, in The 2019 SIGNLL Conference on Computational Natural Language Learning (CoNLL), 2019.
    Full Text BibTeX Details
    @inproceedings{han2019deep,
      title = {Deep Structured Neural Network for Event Temporal Relation Extraction},
      author = {Han, Rujun and Hsu, I and Yang, Mu and Galstyan, Aram and Weischedel, Ralph and Peng, Nanyun},
      booktitle = {The 2019 SIGNLL Conference on Computational Natural Language Learning (CoNLL)},
      year = {2019}
    }
    
    Details
  • Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages

    Wasi Uddin Ahmad, Zhisong Zhang, Xuezhe Ma, Kai-Wei Chang, and Nanyun Peng, in The 2019 SIGNLL Conference on Computational Natural Language Learning (CoNLL), 2019.
    Full Text BibTeX Details
    @inproceedings{ahmad2019cross,
      title = {Cross-lingual Dependency Parsing with Unlabeled Auxiliary Languages},
      author = {Ahmad, Wasi Uddin and Zhang, Zhisong and Ma, Xuezhe and Chang, Kai-Wei and Peng, Nanyun},
      booktitle = {The 2019 SIGNLL Conference on Computational Natural Language Learning (CoNLL)},
      year = {2019}
    }
    
    Details
  • Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation

    Xiao Huang, Li Dong, Elizabeth Boschee, and Nanyun Peng, in The 2019 SIGNLL Conference on Computational Natural Language Learning (CoNLL), 2019.
    Full Text BibTeX Details
    @inproceedings{huang2019learning,
      title = {Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient Adaptation},
      author = {Huang, Xiao and Dong, Li and Boschee, Elizabeth and Peng, Nanyun},
      booktitle = {The 2019 SIGNLL Conference on Computational Natural Language Learning (CoNLL)},
      year = {2019}
    }
    
    Details
  • Pun Generation with Surprise

    He He, Nanyun Peng, and Percy Liang, in 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019), 2019.
    Full Text BibTeX Details
    @inproceedings{he2019pun,
      title = {Pun Generation with Surprise},
      author = {He, He and Peng, Nanyun and Liang, Percy},
      booktitle = {2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019)},
      volume = {1},
      year = {2019}
    }
    
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  • On difficulties of cross-lingual transfer with order differences: A case study on dependency parsing

    Wasi Uddin Ahmad, Zhisong Zhang, Xuezhe Ma, Eduard Hovy, Kai-Wei Chang, and Nanyun Peng, in Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2019.
    Full Text BibTeX Details
    @inproceedings{ahmad2019difficulties,
      title = {On difficulties of cross-lingual transfer with order differences: A case study on dependency parsing},
      author = {Ahmad, Wasi Uddin and Zhang, Zhisong and Ma, Xuezhe and Hovy, Eduard and Chang, Kai-Wei and Peng, Nanyun},
      booktitle = {Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)},
      year = {2019}
    }
    
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  • Plan-And-Write: Towards Better Automatic Storytelling

    Lili Yao, Nanyun Peng, Weischedel Ralph, Kevin Knight, Dongyan Zhao, and Rui Yan, in The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19), 2019.
    Full Text BibTeX Details
    @inproceedings{yao2019plan,
      title = {Plan-And-Write: Towards Better Automatic Storytelling},
      author = {Yao, Lili and Peng, Nanyun and Ralph, Weischedel and Knight, Kevin and Zhao, Dongyan and Yan, Rui},
      booktitle = {The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19)},
      year = {2019}
    }
    
    Details
  • Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation

    Seraphina Goldfarb-Tarrant, Haining Feng, and Nanyun Peng, in 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019), Demonstrations Track, 2019.
    Full Text Video Code Abstract BibTeX Details
    Story composition is a challenging problem for machines and even for humans. We present a neural narrative generation system that interacts with humans to generate stories. Our system has different levels of human interaction, which enables us to understand at what stage of story-writing human collaboration is most productive, both to improving story quality and human engagement in the writing process. We compare different varieties of interaction in story-writing, story-planning, and diversity controls under time constraints, and show that increased types of human collaboration at both planning and writing stages results in a 10-50% improvement in story quality as compared to less interactive baselines. We also show an accompanying increase in user engagement and satisfaction with stories as compared to our own less interactive systems and to previous turn-taking approaches to interaction. Finally, we find that humans tasked with collaboratively improving a particular characteristic of a story are in fact able to do so, which has implications for future uses of human-in-the-loop systems.
    @inproceedings{goldfarb2019plan,
      title = {Plan, Write, and Revise: an Interactive System for Open-Domain Story Generation},
      author = {Goldfarb-Tarrant, Seraphina and Feng, Haining and Peng, Nanyun},
      booktitle = {2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019), Demonstrations Track},
      volume = {4},
      pages = {89--97},
      year = {2019}
    }
    
    Details
  • Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings

    Sarik Ghazarian, Johnny Tian-Zheng Wei, Aram Galstyan, and Nanyun Peng, in 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019), NeuralGen Workshop, 2019.
    Full Text BibTeX Details
    @inproceedings{ghazarian2019better,
      title = {Better Automatic Evaluation of Open-Domain Dialogue Systems with Contextualized Embeddings},
      author = {Ghazarian, Sarik and Wei, Johnny Tian-Zheng and Galstyan, Aram and Peng, Nanyun},
      booktitle = {2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019), NeuralGen Workshop},
      year = {2019}
    }
    
    Details
  • Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding

    Rujun Han, Mengyue Liang, Bashar Alhafni, and Nanyun Peng, in 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019), Workshop on Narrative Understanding, 2019.
    Full Text BibTeX Details
    @inproceedings{han2019contextualized,
      title = {Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding},
      author = {Han, Rujun and Liang, Mengyue and Alhafni, Bashar and Peng, Nanyun},
      booktitle = {2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2019), Workshop on Narrative Understanding},
      year = {2019}
    }
    
    Details
  • Building deep learning models for evidence classification from the open access biomedical literature

    Gully A. Burns, Xiangci Li, and Nanyun Peng, Database, 2019.
    Full Text BibTeX Details
    @article{burns2019building,
      title = {Building deep learning models for evidence classification from the open access biomedical literature},
      author = {Burns, Gully A and Li, Xiangci and Peng, Nanyun},
      journal = {Database},
      year = {2019},
      publisher = {Narnia}
    }
    
    Details
  • Espresso: A Fast End-to-end Neural Speech Recognition Toolkit

    Yiming Wang, Tongfei Chen, Hainan Xu, Shuoyang Ding, Hang Lv, Yiwen Shao, Nanyun Peng, Lei Xie, Shinji Watanabe, and Sanjeev Khudanpur, in The 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), 2019.
    Full Text BibTeX Details
    @inproceedings{wang2019espresso,
      title = {Espresso: A Fast End-to-end Neural Speech Recognition Toolkit},
      author = {Wang, Yiming and Chen, Tongfei and Xu, Hainan and Ding, Shuoyang and Lv, Hang and Shao, Yiwen and Peng, Nanyun and Xie, Lei and Watanabe, Shinji and Khudanpur, Sanjeev},
      booktitle = {The 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
      year = {2019}
    }
    
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  • Evaluating and Enhancing the Robustness of Retrieval-Based Dialogue Systems with Adversarial Examples

    Jia Li, Chongyang Tao, Nanyun Peng, Wei Wu, Dongyan Zhao, and Rui Yan, in CCF International Conference on Natural Language Processing and Chinese Computing, 2019.
    Full Text BibTeX Details
    @inproceedings{li2019evaluating,
      title = {Evaluating and Enhancing the Robustness of Retrieval-Based Dialogue Systems with Adversarial Examples},
      author = {Li, Jia and Tao, Chongyang and Peng, Nanyun and Wu, Wei and Zhao, Dongyan and Yan, Rui},
      booktitle = {CCF International Conference on Natural Language Processing and Chinese Computing},
      pages = {142--154},
      year = {2019},
      organization = {Springer}
    }
    
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  • Debiasing Community Detection: The Importance of Lowly-Connected Nodes

    Ninareh Mehrabi, Fred Morstatter, Nanyun Peng, and Aram Galstyan, in The 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019), 2019.
    Full Text BibTeX Details
    @inproceedings{mehrabi2019debiasing,
      title = {Debiasing Community Detection: The Importance of Lowly-Connected Nodes},
      author = {Mehrabi, Ninareh and Morstatter, Fred and Peng, Nanyun and Galstyan, Aram},
      booktitle = {The 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)},
      year = {2019}
    }
    
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2018

  • Towards controllable story generation

    Nanyun Peng, Marjan Ghazvininejad, Jonathan May, and Kevin Knight, in NAACL Workshop, 2018.
    Full Text BibTeX Details
    @inproceedings{peng2018towards,
      title = {Towards controllable story generation},
      author = {Peng, Nanyun and Ghazvininejad, Marjan and May, Jonathan and Knight, Kevin},
      booktitle = {NAACL Workshop},
      year = {2018}
    }
    
    Details
  • Learning to Converse with Noisy Data: Generation with Calibration.

    Mingyue Shang, Zhenxin Fu, Nanyun Peng, Yansong Feng, Dongyan Zhao, and Rui Yan, in IJCAI, 2018.
    Full Text BibTeX Details
    @inproceedings{shang2018learning,
      title = {Learning to Converse with Noisy Data: Generation with Calibration.},
      author = {Shang, Mingyue and Fu, Zhenxin and Peng, Nanyun and Feng, Yansong and Zhao, Dongyan and Yan, Rui},
      booktitle = {IJCAI},
      pages = {4338--4344},
      year = {2018}
    }
    
    Details
  • Scalable Construction and Reasoning of Massive Knowledge Bases

    Xiang Ren, Nanyun Peng, and William Yang Wang, in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts, 2018.
    Full Text BibTeX Details
    @inproceedings{ren2018scalable,
      title = {Scalable Construction and Reasoning of Massive Knowledge Bases},
      author = {Ren, Xiang and Peng, Nanyun and Wang, William Yang},
      booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts},
      pages = {10--16},
      year = {2018}
    }
    
    Details
  • Stack-pointer networks for dependency parsing

    Xuezhe Ma, Zecong Hu, Jingzhou Liu, Nanyun Peng, Graham Neubig, and Eduard Hovy, in The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), 2018.
    Full Text BibTeX Details
    @inproceedings{ma2018stack,
      title = {Stack-pointer networks for dependency parsing},
      author = {Ma, Xuezhe and Hu, Zecong and Liu, Jingzhou and Peng, Nanyun and Neubig, Graham and Hovy, Eduard},
      booktitle = {The 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018)},
      volume = {1},
      year = {2018}
    }
    
    Details
  • Style Transfer in Text: Exploration and Evaluation

    Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, and Rui Yan, in Proceedings of The Thirty-Second Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence (AAAI), 2018.
    Full Text BibTeX Details
    @inproceedings{fu2018style,
      title = {Style Transfer in Text: Exploration and Evaluation},
      author = {Fu, Zhenxin and Tan, Xiaoye and Peng, Nanyun and Zhao, Dongyan and Yan, Rui},
      booktitle = {Proceedings of The Thirty-Second Association for the Advancement of Artificial Intelligence Conference on Artificial Intelligence (AAAI)},
      year = {2018}
    }
    
    Details

2017

2016

2015

  • HLTCOE Participation in TAC KBP 2015: Cold Start and TEDL

    Taneeya Satyapanich, Tim Finin, Paul McNamee, James Mayfield, Doug Oard, Nanyun Peng, Ning Gao, Yiu-Chang Lin, Joshi MacKin, and Tim Dowd, UMBC Faculty Collection, 2015.
    BibTeX Details
    @article{satyapanich2015hltcoe,
      title = {HLTCOE Participation in TAC KBP 2015: Cold Start and TEDL},
      author = {Satyapanich, Taneeya and Finin, Tim and McNamee, Paul and Mayfield, James and Oard, Doug and Peng, Nanyun and Gao, Ning and Lin, Yiu-Chang and MacKin, Joshi and Dowd, Tim},
      journal = {UMBC Faculty Collection},
      year = {2015},
      publisher = {National Institute of Standards and Technology}
    }
    
    Details
  • Named entity recognition for chinese social media with jointly trained embeddings

    Nanyun Peng and Mark Dredze, in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015.
    Full Text BibTeX Details
    @inproceedings{peng2015named,
      title = {Named entity recognition for chinese social media with jointly trained embeddings},
      author = {Peng, Nanyun and Dredze, Mark},
      booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
      pages = {548--554},
      year = {2015}
    }
    
    Details
  • An Empirical Study of Chinese Name Matching and Applications

    Nanyun Peng, Mo Yu, and Mark Dredze, in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL), 2015.
    BibTeX Details
    @inproceedings{peng2015empirical,
      title = {An Empirical Study of Chinese Name Matching and Applications},
      author = {Peng, Nanyun and Yu, Mo and Dredze, Mark},
      booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL)},
      year = {2015}
    }
    
    Details
  • Modeling word forms using latent underlying morphs and phonology

    Ryan Cotterell, Nanyun Peng, and Jason Eisner, Transactions of the Association of Computational Linguistics, 2015.
    Full Text BibTeX Details
    @article{cotterell2015modeling,
      title = {Modeling word forms using latent underlying morphs and phonology},
      author = {Cotterell, Ryan and Peng, Nanyun and Eisner, Jason},
      journal = {Transactions of the Association of Computational Linguistics},
      volume = {3},
      number = {1},
      year = {2015}
    }
    
    Details
  • A concrete chinese NLP pipeline

    Nanyun Peng, Francis Ferraro, Mo Yu, Nicholas Andrews, Jay DeYoung, Max Thomas, Matthew R. Gormley, Travis Wolfe, Craig Harman, Benjamin Van Durme, and others, in Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations, 2015.
    BibTeX Details
    @inproceedings{peng2015concrete,
      title = {A concrete chinese NLP pipeline},
      author = {Peng, Nanyun and Ferraro, Francis and Yu, Mo and Andrews, Nicholas and DeYoung, Jay and Thomas, Max and Gormley, Matthew R and Wolfe, Travis and Harman, Craig and Van Durme, Benjamin and others},
      booktitle = {Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations},
      pages = {86--90},
      year = {2015}
    }
    
    Details
  • A chinese concrete nlp pipeline

    Nanyun Peng, Francis Ferraro, Mo Yu, Nicholas Andrews, Jay DeYoung, Max Thomas, Matt Gormley, Travis Wolfe, Craig Harman, Benjamin Van Durme, and others, North American Chapter of the Association for Computational Linguistics (NAACL), Demonstration Session, 2015.
    BibTeX Details
    @article{peng2015chinese,
      title = {A chinese concrete nlp pipeline},
      author = {Peng, Nanyun and Ferraro, Francis and Yu, Mo and Andrews, Nicholas and DeYoung, Jay and Thomas, Max and Gormley, Matt and Wolfe, Travis and Harman, Craig and Van Durme, Benjamin and others},
      journal = {North American Chapter of the Association for Computational Linguistics (NAACL), Demonstration Session},
      year = {2015}
    }
    
    Details
  • HLTCOE participation in TAC KBP 2015: Cold start and TEDL

    Tim Finin, Dawn Lawrie, Paul McNamee, James Mayfield, Doug Oard, Nanyun Peng, Ning Gao, Yiu-Chang Lin, Joshi MacKin, Tim Dowd, and others, in Eighth Text Analysis Conference, 2015.
    BibTeX Details
    @inproceedings{finin2015hltcoe,
      title = {HLTCOE participation in TAC KBP 2015: Cold start and TEDL},
      author = {Finin, Tim and Lawrie, Dawn and McNamee, Paul and Mayfield, James and Oard, Doug and Peng, Nanyun and Gao, Ning and Lin, Yiu-Chang and MacKin, Joshi and Dowd, Tim and others},
      booktitle = {Eighth Text Analysis Conference},
      year = {2015}
    }
    
    Details
  • Dual decomposition inference for graphical models over strings

    Nanyun Peng, Ryan Cotterell, and Jason Eisner, in Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 2015.
    Full Text BibTeX Details
    @inproceedings{peng2015dual,
      title = {Dual decomposition inference for graphical models over strings},
      author = {Peng, Nanyun and Cotterell, Ryan and Eisner, Jason},
      booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
      pages = {917--927},
      year = {2015}
    }
    
    Details

2014

  • Stochastic Contextual Edit Distance and Probabilistic FSTs

    Ryan Cotterell, Nanyun Peng, and Jason Eisner, in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 2014.
    Full Text BibTeX Details
    @inproceedings{cotterell2014stochastic,
      title = {Stochastic Contextual Edit Distance and Probabilistic FSTs},
      author = {Cotterell, Ryan and Peng, Nanyun and Eisner, Jason},
      booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics},
      year = {2014}
    }
    
    Details
  • Learning polylingual topic models from code-switched social media documents

    Nanyun Peng, Yiming Wang, and Mark Dredze, in Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2014.
    Full Text BibTeX Details
    @inproceedings{peng2014learning,
      title = {Learning polylingual topic models from code-switched social media documents},
      author = {Peng, Nanyun and Wang, Yiming and Dredze, Mark},
      booktitle = {Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
      pages = {674--679},
      year = {2014}
    }
    
    Details

2012

  • Online Plagiarized Detection Through Exploiting Lexical, Syntax, and Semantic Information

    Wan-Yu Lin, Nanyun Peng, Chun-Chao Yen, and Shou-de Lin, in Proceedings of the ACL 2012 System Demonstrations, 2012.
    BibTeX Details
    @inproceedings{lin2012online,
      title = {Online Plagiarized Detection Through Exploiting Lexical, Syntax, and Semantic Information},
      author = {Lin, Wan-Yu and Peng, Nanyun and Yen, Chun-Chao and Lin, Shou-de},
      booktitle = {Proceedings of the ACL 2012 System Demonstrations},
      pages = {145--150},
      year = {2012}
    }
    
    Details
  • Exploiting latent information to predict diffusions of novel topics on social networks

    Tsung-Ting Kuo, San-Chuan Hung, Wei-Shih Lin, Nanyun Peng, Shou-De Lin, and Wei-Fen Lin, in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2012.
    BibTeX Details
    @inproceedings{kuo2012exploiting,
      title = {Exploiting latent information to predict diffusions of novel topics on social networks},
      author = {Kuo, Tsung-Ting and Hung, San-Chuan and Lin, Wei-Shih and Peng, Nanyun and Lin, Shou-De and Lin, Wei-Fen},
      booktitle = {Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
      pages = {344--348},
      year = {2012}
    }
    
    Details
  • On convergence rate of concave-convex procedure

    Ian E. H. Yen, Nanyun Peng, Po-Wei Wang, and Shou-De Lin, in Proceedings of the NIPS 2012 Optimization Workshop, 2012.
    BibTeX Details
    @inproceedings{yen2012convergence,
      title = {On convergence rate of concave-convex procedure},
      author = {Yen, Ian EH and Peng, Nanyun and Wang, Po-Wei and Lin, Shou-De},
      booktitle = {Proceedings of the NIPS 2012 Optimization Workshop},
      pages = {31--35},
      year = {2012}
    }
    
    Details