Yuchen Zhang


Value-Agnostic Conversational Semantic Parsing
Emmanouil Antonios Platanios | Adam Pauls | Subhro Roy | Yuchen Zhang | Alexander Kyte | Alan Guo | Sam Thomson | Jayant Krishnamurthy | Jason Wolfe | Jacob Andreas | Dan Klein
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Conversational semantic parsers map user utterances to executable programs given dialogue histories composed of previous utterances, programs, and system responses. Existing parsers typically condition on rich representations of history that include the complete set of values and computations previously discussed. We propose a model that abstracts over values to focus prediction on type- and function-level context. This approach provides a compact encoding of dialogue histories and predicted programs, improving generalization and computational efficiency. Our model incorporates several other components, including an atomic span copy operation and structural enforcement of well-formedness constraints on predicted programs, that are particularly advantageous in the low-data regime. Trained on the SMCalFlow and TreeDST datasets, our model outperforms prior work by 7.3% and 10.6% respectively in terms of absolute accuracy. Trained on only a thousand examples from each dataset, it outperforms strong baselines by 12.4% and 6.4%. These results indicate that simple representations are key to effective generalization in conversational semantic parsing.


Task-Oriented Dialogue as Dataflow Synthesis
Jacob Andreas | John Bufe | David Burkett | Charles Chen | Josh Clausman | Jean Crawford | Kate Crim | Jordan DeLoach | Leah Dorner | Jason Eisner | Hao Fang | Alan Guo | David Hall | Kristin Hayes | Kellie Hill | Diana Ho | Wendy Iwaszuk | Smriti Jha | Dan Klein | Jayant Krishnamurthy | Theo Lanman | Percy Liang | Christopher H. Lin | Ilya Lintsbakh | Andy McGovern | Aleksandr Nisnevich | Adam Pauls | Dmitrij Petters | Brent Read | Dan Roth | Subhro Roy | Jesse Rusak | Beth Short | Div Slomin | Ben Snyder | Stephon Striplin | Yu Su | Zachary Tellman | Sam Thomson | Andrei Vorobev | Izabela Witoszko | Jason Wolfe | Abby Wray | Yuchen Zhang | Alexander Zotov
Transactions of the Association for Computational Linguistics, Volume 8

We describe an approach to task-oriented dialogue in which dialogue state is represented as a dataflow graph. A dialogue agent maps each user utterance to a program that extends this graph. Programs include metacomputation operators for reference and revision that reuse dataflow fragments from previous turns. Our graph-based state enables the expression and manipulation of complex user intents, and explicit metacomputation makes these intents easier for learned models to predict. We introduce a new dataset, SMCalFlow, featuring complex dialogues about events, weather, places, and people. Experiments show that dataflow graphs and metacomputation substantially improve representability and predictability in these natural dialogues. Additional experiments on the MultiWOZ dataset show that our dataflow representation enables an otherwise off-the-shelf sequence-to-sequence model to match the best existing task-specific state tracking model. The SMCalFlow dataset, code for replicating experiments, and a public leaderboard are available at https://www.microsoft.com/en-us/research/project/dataflow-based-dialogue-semantic-machines.


Acquiring Structured Temporal Representation via Crowdsourcing: A Feasibility Study
Yuchen Zhang | Nianwen Xue
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Temporal Dependency Trees are a structured temporal representation that represents temporal relations among time expressions and events in a text as a dependency tree structure. Compared to traditional pair-wise temporal relation representations, temporal dependency trees facilitate efficient annotations, higher inter-annotator agreement, and efficient computations. However, annotations on temporal dependency trees so far have only been done by expert annotators, which is costly and time-consuming. In this paper, we introduce a method to crowdsource temporal dependency tree annotations, and show that this representation is intuitive and can be collected with high accuracy and agreement through crowdsourcing. We produce a corpus of temporal dependency trees, and present a baseline temporal dependency parser, trained and evaluated on this new corpus.


Structured Interpretation of Temporal Relations
Yuchen Zhang | Nianwen Xue
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

Neural Ranking Models for Temporal Dependency Structure Parsing
Yuchen Zhang | Nianwen Xue
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We design and build the first neural temporal dependency parser. It utilizes a neural ranking model with minimal feature engineering, and parses time expressions and events in a text into a temporal dependency tree structure. We evaluate our parser on two domains: news reports and narrative stories. In a parsing-only evaluation setup where gold time expressions and events are provided, our parser reaches 0.81 and 0.70 f-score on unlabeled and labeled parsing respectively, a result that is very competitive against alternative approaches. In an end-to-end evaluation setup where time expressions and events are automatically recognized, our parser beats two strong baselines on both data domains. Our experimental results and discussions shed light on the nature of temporal dependency structures in different domains and provide insights that we believe will be valuable to future research in this area.


Macro Grammars and Holistic Triggering for Efficient Semantic Parsing
Yuchen Zhang | Panupong Pasupat | Percy Liang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

To learn a semantic parser from denotations, a learning algorithm must search over a combinatorially large space of logical forms for ones consistent with the annotated denotations. We propose a new online learning algorithm that searches faster as training progresses. The two key ideas are using macro grammars to cache the abstract patterns of useful logical forms found thus far, and holistic triggering to efficiently retrieve the most relevant patterns based on sentence similarity. On the WikiTableQuestions dataset, we first expand the search space of an existing model to improve the state-of-the-art accuracy from 38.7% to 42.7%, and then use macro grammars and holistic triggering to achieve an 11x speedup and an accuracy of 43.7%.


Automatic Inference of the Tense of Chinese Events Using Implicit Linguistic Information
Yuchen Zhang | Nianwen Xue
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Buy one get one free: Distant annotation of Chinese tense, event type and modality
Nianwen Xue | Yuchen Zhang
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

We describe a “distant annotation” method where we mark up the semantic tense, event type, and modality of Chinese events via a word-aligned parallel corpus. We first map Chinese verbs to their English counterparts via word alignment, and then annotate the resulting English text spans with coarse-grained categories for semantic tense, event type, and modality that we believe apply to both English and Chinese. Because English has richer morpho-syntactic indicators for semantic tense, event type and modality than Chinese, our intuition is that this distant annotation approach will yield more consistent annotation than if we annotate the Chinese side directly. We report experimental results that show stable annotation agreement statistics and that event type and modality have significant influence on tense prediction. We also report the size of the annotated corpus that we have obtained, and how different domains impact annotation consistency.


Distant annotation of Chinese tense and modality
Nianwen Xue | Yuchen Zhang | Yaqin Yang
Proceedings of the IWCS 2013 Workshop on Annotation of Modal Meanings in Natural Language (WAMM)

Towards Robust Linguistic Analysis using OntoNotes
Sameer Pradhan | Alessandro Moschitti | Nianwen Xue | Hwee Tou Ng | Anders Björkelund | Olga Uryupina | Yuchen Zhang | Zhi Zhong
Proceedings of the Seventeenth Conference on Computational Natural Language Learning


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CoNLL-2012 Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes
Sameer Pradhan | Alessandro Moschitti | Nianwen Xue | Olga Uryupina | Yuchen Zhang
Joint Conference on EMNLP and CoNLL - Shared Task