Bishan Yang


Learning to Learn Semantic Parsers from Natural Language Supervision
Igor Labutov | Bishan Yang | Tom Mitchell
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

As humans, we often rely on language to learn language. For example, when corrected in a conversation, we may learn from that correction, over time improving our language fluency. Inspired by this observation, we propose a learning algorithm for training semantic parsers from supervision (feedback) expressed in natural language. Our algorithm learns a semantic parser from users’ corrections such as “no, what I really meant was before his job, not after”, by also simultaneously learning to parse this natural language feedback in order to leverage it as a form of supervision. Unlike supervision with gold-standard logical forms, our method does not require the user to be familiar with the underlying logical formalism, and unlike supervision from denotation, it does not require the user to know the correct answer to their query. This makes our learning algorithm naturally scalable in settings where existing conversational logs are available and can be leveraged as training data. We construct a novel dataset of natural language feedback in a conversational setting, and show that our method is effective at learning a semantic parser from such natural language supervision.

Multi-Relational Question Answering from Narratives: Machine Reading and Reasoning in Simulated Worlds
Igor Labutov | Bishan Yang | Anusha Prakash | Amos Azaria
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge. These two sources have historically shaped the kinds of questions that are asked over these sources, and the methods developed to answer them. In this work, we look towards a practical use-case of QA over user-instructed knowledge that uniquely combines elements of both structured QA over knowledge bases, and unstructured QA over narrative, introducing the task of multi-relational QA over personal narrative. As a first step towards this goal, we make three key contributions: (i) we generate and release TextWorldsQA, a set of five diverse datasets, where each dataset contains dynamic narrative that describes entities and relations in a simulated world, paired with variably compositional questions over that knowledge, (ii) we perform a thorough evaluation and analysis of several state-of-the-art QA models and their variants at this task, and (iii) we release a lightweight Python-based framework we call TextWorlds for easily generating arbitrary additional worlds and narrative, with the goal of allowing the community to create and share a growing collection of diverse worlds as a test-bed for this task.


Leveraging Knowledge Bases in LSTMs for Improving Machine Reading
Bishan Yang | Tom Mitchell
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

This paper focuses on how to take advantage of external knowledge bases (KBs) to improve recurrent neural networks for machine reading. Traditional methods that exploit knowledge from KBs encode knowledge as discrete indicator features. Not only do these features generalize poorly, but they require task-specific feature engineering to achieve good performance. We propose KBLSTM, a novel neural model that leverages continuous representations of KBs to enhance the learning of recurrent neural networks for machine reading. To effectively integrate background knowledge with information from the currently processed text, our model employs an attention mechanism with a sentinel to adaptively decide whether to attend to background knowledge and which information from KBs is useful. Experimental results show that our model achieves accuracies that surpass the previous state-of-the-art results for both entity extraction and event extraction on the widely used ACE2005 dataset.

A Joint Sequential and Relational Model for Frame-Semantic Parsing
Bishan Yang | Tom Mitchell
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We introduce a new method for frame-semantic parsing that significantly improves the prior state of the art. Our model leverages the advantages of a deep bidirectional LSTM network which predicts semantic role labels word by word and a relational network which predicts semantic roles for individual text expressions in relation to a predicate. The two networks are integrated into a single model via knowledge distillation, and a unified graphical model is employed to jointly decode frames and semantic roles during inference. Experiments on the standard FrameNet data show that our model significantly outperforms existing neural and non-neural approaches, achieving a 5.7 F1 gain over the current state of the art, for full frame structure extraction.


Joint Extraction of Events and Entities within a Document Context
Bishan Yang | Tom M. Mitchell
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


A Hierarchical Distance-dependent Bayesian Model for Event Coreference Resolution
Bishan Yang | Claire Cardie | Peter Frazier
Transactions of the Association for Computational Linguistics, Volume 3

We present a novel hierarchical distance-dependent Bayesian model for event coreference resolution. While existing generative models for event coreference resolution are completely unsupervised, our model allows for the incorporation of pairwise distances between event mentions — information that is widely used in supervised coreference models to guide the generative clustering processing for better event clustering both within and across documents. We model the distances between event mentions using a feature-rich learnable distance function and encode them as Bayesian priors for nonparametric clustering. Experiments on the ECB+ corpus show that our model outperforms state-of-the-art methods for both within- and cross-document event coreference resolution.

Conditional Random Fields for Identifying Appropriate Types of Support for Propositions in Online User Comments
Joonsuk Park | Arzoo Katiyar | Bishan Yang
Proceedings of the 2nd Workshop on Argumentation Mining


Context-aware Learning for Sentence-level Sentiment Analysis with Posterior Regularization
Bishan Yang | Claire Cardie
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Typed Tensor Decomposition of Knowledge Bases for Relation Extraction
Kai-Wei Chang | Wen-tau Yih | Bishan Yang | Christopher Meek
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Joint Modeling of Opinion Expression Extraction and Attribute Classification
Bishan Yang | Claire Cardie
Transactions of the Association for Computational Linguistics, Volume 2

In this paper, we study the problems of opinion expression extraction and expression-level polarity and intensity classification. Traditional fine-grained opinion analysis systems address these problems in isolation and thus cannot capture interactions among the textual spans of opinion expressions and their opinion-related properties. We present two types of joint approaches that can account for such interactions during 1) both learning and inference or 2) only during inference. Extensive experiments on a standard dataset demonstrate that our approaches provide substantial improvements over previously published results. By analyzing the results, we gain some insight into the advantages of different joint models.


CPN-CORE: A Text Semantic Similarity System Infused with Opinion Knowledge
Carmen Banea | Yoonjung Choi | Lingjia Deng | Samer Hassan | Michael Mohler | Bishan Yang | Claire Cardie | Rada Mihalcea | Jan Wiebe
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

Joint Inference for Fine-grained Opinion Extraction
Bishan Yang | Claire Cardie
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


Extracting Opinion Expressions with semi-Markov Conditional Random Fields
Bishan Yang | Claire Cardie
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning