Wen-tau Yih

Also published as: Scott Wen-tau Yih


2024

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Few-Shot Data Synthesis for Open Domain Multi-Hop Question Answering
Mingda Chen | Xilun Chen | Wen-tau Yih
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Few-shot learning for open domain multi-hop question answering typically relies on the in-context learning capability of large language models (LLMs). While powerful, these LLMs usually contain tens or hundreds of billions of parameters, making them rather inefficient at inference time. To improve performance of smaller language models, we propose a data synthesis framework for multi-hop question answering that requires less than 10 human-annotated question answer pairs. Our framework depends only on rich, naturally-occurring relationships among documents and is built upon the data generation functions parameterized by LLMs and prompts. We synthesize millions of multi-hop questions and claims to finetune language models, evaluated on popular benchmarks for multi-hop question answering and fact verification. Empirically, our approach improves model performance significantly, allowing the finetuned models to be competitive with GPT-3.5 based approaches while being almost one-third the size in parameter count.

2023

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One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Hongjin Su | Weijia Shi | Jungo Kasai | Yizhong Wang | Yushi Hu | Mari Ostendorf | Wen-tau Yih | Noah A. Smith | Luke Zettlemoyer | Tao Yu
Findings of the Association for Computational Linguistics: ACL 2023

We introduce INSTRUCTOR, a new method for computing text embeddings given task instructions: every text input is embedded together with instructions explaining the use case (e.g., task and domain descriptions). Unlike encoders from prior work that are more specialized, INSTRUCTOR is a single embedder that can generate text embeddings tailored to different downstream tasks and domains, without any further training. We first annotate instructions for 330 diverse tasks and train INSTRUCTOR on this multitask mixture with a contrastive loss. We evaluate INSTRUCTOR on 70 embedding evaluation tasks (66 of which are unseen during training), ranging from classification and information retrieval to semantic textual similarity and text generation evaluation. INSTRUCTOR, while having an order of magnitude fewer parameters than the previous best model, achieves state-of-the-art performance, with an average improvement of 3.4% compared to the previous best results on the 70 diverse datasets. Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets. Our model, code, and data are available at https://instructor-embedding.github.io.

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Nonparametric Masked Language Modeling
Sewon Min | Weijia Shi | Mike Lewis | Xilun Chen | Wen-tau Yih | Hannaneh Hajishirzi | Luke Zettlemoyer
Findings of the Association for Computational Linguistics: ACL 2023

Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases. We introduce NPM, the first nonparametric masked language model that replaces this softmax with a nonparametric distribution over every phrase in a reference corpus. NPM fills in the [MASK] solely from retrieving a token from a text corpus. We show that NPM can be efficiently trained with a contrastive objective and an in-batch approximation to full corpus retrieval. Zero-shot evaluation on 16 tasks including classification, fact probing and question answering demonstrates that NPM outperforms significantly larger parametric models, either with or without a retrieve-and-generate approach. It is particularly better at dealing with rare patterns (word senses or facts) and predicting rare or nearly unseen words (e.g., non-Latin script). We release the model and code at github.com/facebookresearch/NPM.

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Task-aware Retrieval with Instructions
Akari Asai | Timo Schick | Patrick Lewis | Xilun Chen | Gautier Izacard | Sebastian Riedel | Hannaneh Hajishirzi | Wen-tau Yih
Findings of the Association for Computational Linguistics: ACL 2023

We study the problem of retrieval with instructions, where users provide explicit descriptions of their intent along with their queries to guide a retrieval system. Our solution is a general-purpose task-aware retrieval system, trained using multi-task instruction tuning and can follow human-written instructions to find relevant documents to a given query. We introduce the first large-scale collection of 37 retrieval datasets with instructions, BERRI, and present TART, a single multi-task retrieval system trained on BERRI with instructions that can adapt to a new task without any parameter updates. TART advances the state of the art on two zero-shot retrieval benchmarks, BEIR and LOTTE, outperforming models up to three times larger. We further introduce a new evaluation setup, X2-Retrieval, to better reflect real-world scenarios in which diverse domains and tasks are pooled. TART significantly outperforms competitive baselines in this setup, further highlighting the effectiveness of guiding retrieval with instructions.

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Reimagining Retrieval Augmented Language Models for Answering Queries
Wang-Chiew Tan | Yuliang Li | Pedro Rodriguez | Richard James | Xi Victoria Lin | Alon Halevy | Wen-tau Yih
Findings of the Association for Computational Linguistics: ACL 2023

We present a reality check on large language models and inspect the promise of retrieval-augmented language models in comparison. Such language models are semi-parametric, where models integrate model parameters and knowledge from external data sources to make their predictions, as opposed to the parametric nature of vanilla large language models. We give initial experimental findings that semi-parametric architectures can be enhanced with views, a query analyzer/planner, and provenance to make a significantly more powerful system for question answering in terms of accuracy and efficiency, and potentially for other NLP tasks.

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Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering
Yung-Sung Chuang | Wei Fang | Shang-Wen Li | Wen-tau Yih | James Glass
Findings of the Association for Computational Linguistics: ACL 2023

We propose EAR, a query Expansion And Reranking approach for improving passage retrieval, with the application to open-domain question answering. EAR first applies a query expansion model to generate a diverse set of queries, and then uses a query reranker to select the ones that could lead to better retrieval results. Motivated by the observation that the best query expansion often is not picked by greedy decoding, EAR trains its reranker to predict the rank orders of the gold passages when issuing the expanded queries to a given retriever. By connecting better the query expansion model and retriever, EAR significantly enhances a traditional sparse retrieval method, BM25. Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points in in-domain and out-of-domain settings, respectively, when compared to a vanilla query expansion model, GAR, and a dense retrieval model, DPR.

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How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval
Sheng-Chieh Lin | Akari Asai | Minghan Li | Barlas Oguz | Jimmy Lin | Yashar Mehdad | Wen-tau Yih | Xilun Chen
Findings of the Association for Computational Linguistics: EMNLP 2023

Various techniques have been developed in recent years to improve dense retrieval (DR), such as unsupervised contrastive learning and pseudo-query generation. Existing DRs, however, often suffer from effectiveness tradeoffs between supervised and zero-shot retrieval, which some argue was due to the limited model capacity. We contradict this hypothesis and show that a generalizable DR can be trained to achieve high accuracy in both supervised and zero-shot retrieval without increasing model size. In particular, we systematically examine the contrastive learning of DRs, under the framework of Data Augmentation (DA). Our study shows that common DA practices such as query augmentation with generative models and pseudo-relevance label creation using a cross-encoder, are often inefficient and sub-optimal. We hence propose a new DA approach with diverse queries and sources of supervision to progressively train a generalizable DR. As a result, DRAGON, our Dense Retriever trained with diverse AuGmentatiON, is the first BERT-base-sized DR to achieve state-of-the-art effectiveness in both supervised and zero-shot evaluations and even competes with models using more complex late interaction.

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RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering
Victor Zhong | Weijia Shi | Wen-tau Yih | Luke Zettlemoyer
Findings of the Association for Computational Linguistics: EMNLP 2023

We introduce RoMQA, the first benchmark for robust, multi-evidence, multi-answer question answering (QA). RoMQA contains clusters of questions that are derived from related constraints mined from the Wikidata knowledge graph. RoMQA evaluates robustness of QA models to varying constraints by measuring worst-case performance within each question cluster. Compared to prior QA datasets, RoMQA has more human-written questions that require reasoning over more evidence text and have, on average, many more correct answers. In addition, human annotators rate RoMQA questions as more natural or likely to be asked by people. We evaluate state-of-the-art large language models in zero-shot, few-shot, and fine-tuning settings, and find that RoMQA is challenging: zeroshot and few-shot models perform similarly to naive baselines, while supervised retrieval methods perform well below gold evidence upper bounds. Moreover, existing models are not robust to variations in question constraints, but can be made more robust by tuning on clusters of related questions. Our results show that RoMQA is a challenging benchmark for large language models, and provides a quantifiable test to build more robust QA methods.

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FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
Sewon Min | Kalpesh Krishna | Xinxi Lyu | Mike Lewis | Wen-tau Yih | Pang Koh | Mohit Iyyer | Luke Zettlemoyer | Hannaneh Hajishirzi
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Evaluating the factuality of long-form text generated by large language models (LMs) is non-trivial because (1) generations often contain a mixture of supported and unsupported pieces of information, making binary judgments of quality inadequate, and (2) human evaluation is time-consuming and costly. In this paper, we introduce FACTSCORE, a new evaluation that breaks a generation into a series of atomic facts and computes the percentage of atomic facts supported by a reliable knowledge source. We conduct an extensive human evaluation to obtain FACTSCOREs of people biographies generated by several state-of-the-art commercial LMs—InstructGPT, ChatGPT, and the retrieval-augmented PerplexityAI—and report new analysis demonstrating the need for such a fine-grained score (e.g., ChatGPT only achieves 58%). Since human evaluation is costly, we also introduce an automated model that estimates FACTSCORE using retrieval and a strong language model, with less than a 2% error rate. Finally, we use this automated metric to evaluate 6,500 generations from a new set of 13 recent LMs that would have cost $26K if evaluated by humans, with various findings: GPT-4 and ChatGPT are more factual than public models, and Vicuna and Alpaca are some of the best public models. FACTSCORE is available for public use via ‘pip install factscore‘.

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Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing
Hao Yan | Saurabh Srivastava | Yintao Tai | Sida I. Wang | Wen-tau Yih | Ziyu Yao
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Interactive semantic parsing based on natural language (NL) feedback, where users provide feedback to correct the parser mistakes, has emerged as a more practical scenario than the traditional one-shot semantic parsing. However, prior work has heavily relied on human-annotated feedback data to train the interactive semantic parser, which is prohibitively expensive and not scalable. In this work, we propose a new task of simulating NL feedback for interactive semantic parsing. We accompany the task with a novel feedback evaluator. The evaluator is specifically designed to assess the quality of the simulated feedback, based on which we decide the best feedback simulator from our proposed variants. On a text-to-SQL dataset, we show that our feedback simulator can generate high-quality NL feedback to boost the error correction ability of a specific parser. In low-data settings, our feedback simulator can help achieve comparable error correction performance as trained using the costly, full set of human annotations.

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CITADEL: Conditional Token Interaction via Dynamic Lexical Routing for Efficient and Effective Multi-Vector Retrieval
Minghan Li | Sheng-Chieh Lin | Barlas Oguz | Asish Ghoshal | Jimmy Lin | Yashar Mehdad | Wen-tau Yih | Xilun Chen
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multi-vector retrieval methods combine the merits of sparse (e.g. BM25) and dense (e.g. DPR) retrievers and have achieved state-of-the-art performance on various retrieval tasks. These methods, however, are orders of magnitude slower and need much more space to store their indices compared to their single-vector counterparts. In this paper, we unify different multi-vector retrieval models from a token routing viewpoint and propose conditional token interaction via dynamic lexical routing, namely CITADEL, for efficient and effective multi-vector retrieval.CITADEL learns to route different token vectors to the predicted lexical keys such that a query token vector only interacts with document token vectors routed to the same key. This design significantly reduces the computation cost while maintaining high accuracy. Notably, CITADEL achieves the same or slightly better performance than the previous state of the art, ColBERT-v2, on both in-domain (MS MARCO) and out-of-domain (BEIR) evaluations, while being nearly 40 times faster. Source code and data are available at https://github.com/facebookresearch/dpr-scale/tree/citadel.

2022

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Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?
Xilun Chen | Kushal Lakhotia | Barlas Oguz | Anchit Gupta | Patrick Lewis | Stan Peshterliev | Yashar Mehdad | Sonal Gupta | Wen-tau Yih
Findings of the Association for Computational Linguistics: EMNLP 2022

Despite their recent popularity and well-known advantages, dense retrievers still lag behind sparse methods such as BM25 in their ability to reliably match salient phrases and rare entities in the query and to generalize to out-of-domain data. It has been argued that this is an inherent limitation of dense models. We rebut this claim by introducing the Salient Phrase Aware Retriever (SPAR), a dense retriever with the lexical matching capacity of a sparse model. We show that a dense Lexical Model Λ can be trained to imitate a sparse one, and SPAR is built by augmenting a standard dense retriever with Λ. Empirically, SPAR shows superior performance on a range of tasks including five question answering datasets, MS MARCO passage retrieval, as well as the EntityQuestions and BEIR benchmarks for out-of-domain evaluation, exceeding the performance of state-of-the-art dense and sparse retrievers. The code and models of SPAR are available at: https://github.com/facebookresearch/dpr-scale/tree/main/spar

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Improving Passage Retrieval with Zero-Shot Question Generation
Devendra Sachan | Mike Lewis | Mandar Joshi | Armen Aghajanyan | Wen-tau Yih | Joelle Pineau | Luke Zettlemoyer
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

We propose a simple and effective re-ranking method for improving passage retrieval in open question answering. The re-ranker re-scores retrieved passages with a zero-shot question generation model, which uses a pre-trained language model to compute the probability of the input question conditioned on a retrieved passage. This approach can be applied on top of any retrieval method (e.g. neural or keyword-based), does not require any domain- or task-specific training (and therefore is expected to generalize better to data distribution shifts), and provides rich cross-attention between query and passage (i.e. it must explain every token in the question). When evaluated on a number of open-domain retrieval datasets, our re-ranker improves strong unsupervised retrieval models by 6%-18% absolute and strong supervised models by up to 12% in terms of top-20 passage retrieval accuracy. We also obtain new state-of-the-art results on full open-domain question answering by simply adding the new re-ranker to existing models with no further changes.

2021

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RECONSIDER: Improved Re-Ranking using Span-Focused Cross-Attention for Open Domain Question Answering
Srinivasan Iyer | Sewon Min | Yashar Mehdad | Wen-tau Yih
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

State-of-the-art Machine Reading Comprehension (MRC) models for Open-domain Question Answering (QA) are typically trained for span selection using distantly supervised positive examples and heuristically retrieved negative examples. This training scheme possibly explains empirical observations that these models achieve a high recall amongst their top few predictions, but a low overall accuracy, motivating the need for answer re-ranking. We develop a successful re-ranking approach (RECONSIDER) for span-extraction tasks that improves upon the performance of MRC models, even beyond large-scale pre-training. RECONSIDER is trained on positive and negative examples extracted from high confidence MRC model predictions, and uses in-passage span annotations to perform span-focused re-ranking over a smaller candidate set. As a result, RECONSIDER learns to eliminate close false positives, achieving a new extractive state of the art on four QA tasks, with 45.5% Exact Match accuracy on Natural Questions with real user questions, and 61.7% on TriviaQA. We will release all related data, models, and code.

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On Unifying Misinformation Detection
Nayeon Lee | Belinda Z. Li | Sinong Wang | Pascale Fung | Hao Ma | Wen-tau Yih | Madian Khabsa
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news, and verifying rumors. By grouping these tasks together, UnifiedM2 learns a richer representation of misinformation, which leads to state-of-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UnifiedM2’s learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets and the model’s generalizability to unseen events.

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Findings of the Association for Computational Linguistics: EMNLP 2021
Marie-Francine Moens | Xuanjing Huang | Lucia Specia | Scott Wen-tau Yih
Findings of the Association for Computational Linguistics: EMNLP 2021

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Multi-Task Retrieval for Knowledge-Intensive Tasks
Jean Maillard | Vladimir Karpukhin | Fabio Petroni | Wen-tau Yih | Barlas Oguz | Veselin Stoyanov | Gargi Ghosh
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)

Retrieving relevant contexts from a large corpus is a crucial step for tasks such as open-domain question answering and fact checking. Although neural retrieval outperforms traditional methods like tf-idf and BM25, its performance degrades considerably when applied to out-of-domain data. Driven by the question of whether a neural retrieval model can be _universal_ and perform robustly on a wide variety of problems, we propose a multi-task trained model. Our approach not only outperforms previous methods in the few-shot setting, but also rivals specialised neural retrievers, even when in-domain training data is abundant. With the help of our retriever, we improve existing models for downstream tasks and closely match or improve the state of the art on multiple benchmarks.

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On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study
Divyansh Kaushik | Douwe Kiela | Zachary C. Lipton | Wen-tau Yih
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)

In adversarial data collection (ADC), a human workforce interacts with a model in real time, attempting to produce examples that elicit incorrect predictions. Researchers hope that models trained on these more challenging datasets will rely less on superficial patterns, and thus be less brittle. However, despite ADC’s intuitive appeal, it remains unclear when training on adversarial datasets produces more robust models. In this paper, we conduct a large-scale controlled study focused on question answering, assigning workers at random to compose questions either (i) adversarially (with a model in the loop); or (ii) in the standard fashion (without a model). Across a variety of models and datasets, we find that models trained on adversarial data usually perform better on other adversarial datasets but worse on a diverse collection of out-of-domain evaluation sets. Finally, we provide a qualitative analysis of adversarial (vs standard) data, identifying key differences and offering guidance for future research.

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Joint Verification and Reranking for Open Fact Checking Over Tables
Michael Sejr Schlichtkrull | Vladimir Karpukhin | Barlas Oguz | Mike Lewis | Wen-tau Yih | Sebastian Riedel
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)

Structured information is an important knowledge source for automatic verification of factual claims. Nevertheless, the majority of existing research into this task has focused on textual data, and the few recent inquiries into structured data have been for the closed-domain setting where appropriate evidence for each claim is assumed to have already been retrieved. In this paper, we investigate verification over structured data in the open-domain setting, introducing a joint reranking-and-verification model which fuses evidence documents in the verification component. Our open-domain model achieves performance comparable to the closed-domain state-of-the-art on the TabFact dataset, and demonstrates performance gains from the inclusion of multiple tables as well as a significant improvement over a heuristic retrieval baseline.

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Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Marie-Francine Moens | Xuanjing Huang | Lucia Specia | Scott Wen-tau Yih
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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On the Influence of Masking Policies in Intermediate Pre-training
Qinyuan Ye | Belinda Z. Li | Sinong Wang | Benjamin Bolte | Hao Ma | Wen-tau Yih | Xiang Ren | Madian Khabsa
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Current NLP models are predominantly trained through a two-stage “pre-train then fine-tune” pipeline. Prior work has shown that inserting an intermediate pre-training stage, using heuristic masking policies for masked language modeling (MLM), can significantly improve final performance. However, it is still unclear (1) in what cases such intermediate pre-training is helpful, (2) whether hand-crafted heuristic objectives are optimal for a given task, and (3) whether a masking policy designed for one task is generalizable beyond that task. In this paper, we perform a large-scale empirical study to investigate the effect of various masking policies in intermediate pre-training with nine selected tasks across three categories. Crucially, we introduce methods to automate the discovery of optimal masking policies via direct supervision or meta-learning. We conclude that the success of intermediate pre-training is dependent on appropriate pre-train corpus, selection of output format (i.e., masked spans or full sentence), and clear understanding of the role that MLM plays for the downstream task. In addition, we find our learned masking policies outperform the heuristic of masking named entities on TriviaQA, and policies learned from one task can positively transfer to other tasks in certain cases, inviting future research in this direction.

2020

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Language Models as Fact Checkers?
Nayeon Lee | Belinda Z. Li | Sinong Wang | Wen-tau Yih | Hao Ma | Madian Khabsa
Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)

Recent work has suggested that language models (LMs) store both common-sense and factual knowledge learned from pre-training data. In this paper, we leverage this implicit knowledge to create an effective end-to-end fact checker using a solely a language model, without any external knowledge or explicit retrieval components. While previous work on extracting knowledge from LMs have focused on the task of open-domain question answering, to the best of our knowledge, this is the first work to examine the use of language models as fact checkers. In a closed-book setting, we show that our zero-shot LM approach outperforms a random baseline on the standard FEVER task, and that our finetuned LM compares favorably with standard baselines. Though we do not ultimately outperform methods which use explicit knowledge bases, we believe our exploration shows that this method is viable and has much room for exploration.

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Blockwise Self-Attention for Long Document Understanding
Jiezhong Qiu | Hao Ma | Omer Levy | Wen-tau Yih | Sinong Wang | Jie Tang
Findings of the Association for Computational Linguistics: EMNLP 2020

We present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both memory consumption and training/inference time, which also enables attention heads to capture either short- or long-range contextual information. We conduct experiments on language model pre-training and several benchmark question answering datasets with various paragraph lengths. BlockBERT uses 18.7-36.1% less memory and 12.0-25.1% less time to learn the model. During testing, BlockBERT saves 27.8% inference time, while having comparable and sometimes better prediction accuracy, compared to an advanced BERT-based model, RoBERTa.

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Proceedings of the First Workshop on Natural Language Interfaces
Ahmed Hassan Awadallah | Yu Su | Huan Sun | Scott Wen-tau Yih
Proceedings of the First Workshop on Natural Language Interfaces

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TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
Pengcheng Yin | Graham Neubig | Wen-tau Yih | Sebastian Riedel
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Recent years have witnessed the burgeoning of pretrained language models (LMs) for text-based natural language (NL) understanding tasks. Such models are typically trained on free-form NL text, hence may not be suitable for tasks like semantic parsing over structured data, which require reasoning over both free-form NL questions and structured tabular data (e.g., database tables). In this paper we present TaBERT, a pretrained LM that jointly learns representations for NL sentences and (semi-)structured tables. TaBERT is trained on a large corpus of 26 million tables and their English contexts. In experiments, neural semantic parsers using TaBERT as feature representation layers achieve new best results on the challenging weakly-supervised semantic parsing benchmark WikiTableQuestions, while performing competitively on the text-to-SQL dataset Spider.

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Open-Domain Question Answering
Danqi Chen | Wen-tau Yih
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

This tutorial provides a comprehensive and coherent overview of cutting-edge research in open-domain question answering (QA), the task of answering questions using a large collection of documents of diversified topics. We will start by first giving a brief historical background, discussing the basic setup and core technical challenges of the research problem, and then describe modern datasets with the common evaluation metrics and benchmarks. The focus will then shift to cutting-edge models proposed for open-domain QA, including two-stage retriever-reader approaches, dense retriever and end-to-end training, and retriever-free methods. Finally, we will cover some hybrid approaches using both text and large knowledge bases and conclude the tutorial with important open questions. We hope that the tutorial will not only help the audience to acquire up-to-date knowledge but also provide new perspectives to stimulate the advances of open-domain QA research in the next phase.

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Efficient One-Pass End-to-End Entity Linking for Questions
Belinda Z. Li | Sewon Min | Srinivasan Iyer | Yashar Mehdad | Wen-tau Yih
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We present ELQ, a fast end-to-end entity linking model for questions, which uses a biencoder to jointly perform mention detection and linking in one pass. Evaluated on WebQSP and GraphQuestions with extended annotations that cover multiple entities per question, ELQ outperforms the previous state of the art by a large margin of +12.7% and +19.6% F1, respectively. With a very fast inference time (1.57 examples/s on a single CPU), ELQ can be useful for downstream question answering systems. In a proof-of-concept experiment, we demonstrate that using ELQ significantly improves the downstream QA performance of GraphRetriever.

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Dense Passage Retrieval for Open-Domain Question Answering
Vladimir Karpukhin | Barlas Oguz | Sewon Min | Patrick Lewis | Ledell Wu | Sergey Edunov | Danqi Chen | Wen-tau Yih
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Open-domain question answering relies on efficient passage retrieval to select candidate contexts, where traditional sparse vector space models, such as TF-IDF or BM25, are the de facto method. In this work, we show that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual-encoder framework. When evaluated on a wide range of open-domain QA datasets, our dense retriever outperforms a strong Lucene-BM25 system greatly by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our end-to-end QA system establish new state-of-the-art on multiple open-domain QA benchmarks.

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An Imitation Game for Learning Semantic Parsers from User Interaction
Ziyu Yao | Yiqi Tang | Wen-tau Yih | Huan Sun | Yu Su
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Despite the widely successful applications, bootstrapping and fine-tuning semantic parsers are still a tedious process with challenges such as costly data annotation and privacy risks. In this paper, we suggest an alternative, human-in-the-loop methodology for learning semantic parsers directly from users. A semantic parser should be introspective of its uncertainties and prompt for user demonstrations when uncertain. In doing so it also gets to imitate the user behavior and continue improving itself autonomously with the hope that eventually it may become as good as the user in interpreting their questions. To combat the sparsity of demonstrations, we propose a novel annotation-efficient imitation learning algorithm, which iteratively collects new datasets by mixing demonstrated states and confident predictions and retrains the semantic parser in a Dataset Aggregation fashion (Ross et al., 2011). We provide a theoretical analysis of its cost bound and also empirically demonstrate its promising performance on the text-to-SQL problem. Code will be available at https://github.com/sunlab-osu/MISP.

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Unsupervised Question Decomposition for Question Answering
Ethan Perez | Patrick Lewis | Wen-tau Yih | Kyunghyun Cho | Douwe Kiela
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We aim to improve question answering (QA) by decomposing hard questions into simpler sub-questions that existing QA systems are capable of answering. Since labeling questions with decompositions is cumbersome, we take an unsupervised approach to produce sub-questions, also enabling us to leverage millions of questions from the internet. Specifically, we propose an algorithm for One-to-N Unsupervised Sequence transduction (ONUS) that learns to map one hard, multi-hop question to many simpler, single-hop sub-questions. We answer sub-questions with an off-the-shelf QA model and give the resulting answers to a recomposition model that combines them into a final answer. We show large QA improvements on HotpotQA over a strong baseline on the original, out-of-domain, and multi-hop dev sets. ONUS automatically learns to decompose different kinds of questions, while matching the utility of supervised and heuristic decomposition methods for QA and exceeding those methods in fluency. Qualitatively, we find that using sub-questions is promising for shedding light on why a QA system makes a prediction.

2019

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Be Consistent! Improving Procedural Text Comprehension using Label Consistency
Xinya Du | Bhavana Dalvi | Niket Tandon | Antoine Bosselut | Wen-tau Yih | Peter Clark | Claire Cardie
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Our goal is procedural text comprehension, namely tracking how the properties of entities (e.g., their location) change with time given a procedural text (e.g., a paragraph about photosynthesis, a recipe). This task is challenging as the world is changing throughout the text, and despite recent advances, current systems still struggle with this task. Our approach is to leverage the fact that, for many procedural texts, multiple independent descriptions are readily available, and that predictions from them should be consistent (label consistency). We present a new learning framework that leverages label consistency during training, allowing consistency bias to be built into the model. Evaluation on a standard benchmark dataset for procedural text, ProPara (Dalvi et al., 2018), shows that our approach significantly improves prediction performance (F1) over prior state-of-the-art systems.

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Everything Happens for a Reason: Discovering the Purpose of Actions in Procedural Text
Bhavana Dalvi | Niket Tandon | Antoine Bosselut | Wen-tau Yih | Peter Clark
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Our goal is to better comprehend procedural text, e.g., a paragraph about photosynthesis, by not only predicting what happens, but *why* some actions need to happen before others. Our approach builds on a prior process comprehension framework for predicting actions’ effects, to also identify subsequent steps that those effects enable. We present our new model (XPAD) that biases effect predictions towards those that (1) explain more of the actions in the paragraph and (2) are more plausible with respect to background knowledge. We also extend an existing benchmark dataset for procedural text comprehension, ProPara, by adding the new task of explaining actions by predicting their dependencies. We find that XPAD significantly outperforms prior systems on this task, while maintaining the performance on the original task in ProPara. The dataset is available at http://data.allenai.org/propara

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Model-based Interactive Semantic Parsing: A Unified Framework and A Text-to-SQL Case Study
Ziyu Yao | Yu Su | Huan Sun | Wen-tau Yih
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

As a promising paradigm, interactive semantic parsing has shown to improve both semantic parsing accuracy and user confidence in the results. In this paper, we propose a new, unified formulation of the interactive semantic parsing problem, where the goal is to design a model-based intelligent agent. The agent maintains its own state as the current predicted semantic parse, decides whether and where human intervention is needed, and generates a clarification question in natural language. A key part of the agent is a world model: it takes a percept (either an initial question or subsequent feedback from the user) and transitions to a new state. We then propose a simple yet remarkably effective instantiation of our framework, demonstrated on two text-to-SQL datasets (WikiSQL and Spider) with different state-of-the-art base semantic parsers. Compared to an existing interactive semantic parsing approach that treats the base parser as a black box, our approach solicits less user feedback but yields higher run-time accuracy.

2018

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Reasoning about Actions and State Changes by Injecting Commonsense Knowledge
Niket Tandon | Bhavana Dalvi | Joel Grus | Wen-tau Yih | Antoine Bosselut | Peter Clark
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Comprehending procedural text, e.g., a paragraph describing photosynthesis, requires modeling actions and the state changes they produce, so that questions about entities at different timepoints can be answered. Although several recent systems have shown impressive progress in this task, their predictions can be globally inconsistent or highly improbable. In this paper, we show how the predicted effects of actions in the context of a paragraph can be improved in two ways: (1) by incorporating global, commonsense constraints (e.g., a non-existent entity cannot be destroyed), and (2) by biasing reading with preferences from large-scale corpora (e.g., trees rarely move). Unlike earlier methods, we treat the problem as a neural structured prediction task, allowing hard and soft constraints to steer the model away from unlikely predictions. We show that the new model significantly outperforms earlier systems on a benchmark dataset for procedural text comprehension (+8% relative gain), and that it also avoids some of the nonsensical predictions that earlier systems make.

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Dissecting Contextual Word Embeddings: Architecture and Representation
Matthew E. Peters | Mark Neumann | Luke Zettlemoyer | Wen-tau Yih
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Contextual word representations derived from pre-trained bidirectional language models (biLMs) have recently been shown to provide significant improvements to the state of the art for a wide range of NLP tasks. However, many questions remain as to how and why these models are so effective. In this paper, we present a detailed empirical study of how the choice of neural architecture (e.g. LSTM, CNN, or self attention) influences both end task accuracy and qualitative properties of the representations that are learned. We show there is a tradeoff between speed and accuracy, but all architectures learn high quality contextual representations that outperform word embeddings for four challenging NLP tasks. Additionally, all architectures learn representations that vary with network depth, from exclusively morphological based at the word embedding layer through local syntax based in the lower contextual layers to longer range semantics such coreference at the upper layers. Together, these results suggest that unsupervised biLMs, independent of architecture, are learning much more about the structure of language than previously appreciated.

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QuAC: Question Answering in Context
Eunsol Choi | He He | Mohit Iyyer | Mark Yatskar | Wen-tau Yih | Yejin Choi | Percy Liang | Luke Zettlemoyer
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as we show in a detailed qualitative evaluation. We also report results for a number of reference models, including a recently state-of-the-art reading comprehension architecture extended to model dialog context. Our best model underperforms humans by 20 F1, suggesting that there is significant room for future work on this data. Dataset, baseline, and leaderboard available at http://quac.ai.

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Policy Shaping and Generalized Update Equations for Semantic Parsing from Denotations
Dipendra Misra | Ming-Wei Chang | Xiaodong He | Wen-tau Yih
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Semantic parsing from denotations faces two key challenges in model training: (1) given only the denotations (e.g., answers), search for good candidate semantic parses, and (2) choose the best model update algorithm. We propose effective and general solutions to each of them. Using policy shaping, we bias the search procedure towards semantic parses that are more compatible to the text, which provide better supervision signals for training. In addition, we propose an update equation that generalizes three different families of learning algorithms, which enables fast model exploration. When experimented on a recently proposed sequential question answering dataset, our framework leads to a new state-of-the-art model that outperforms previous work by 5.0% absolute on exact match accuracy.

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Tracking State Changes in Procedural Text: a Challenge Dataset and Models for Process Paragraph Comprehension
Bhavana Dalvi | Lifu Huang | Niket Tandon | Wen-tau Yih | Peter Clark
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We present a new dataset and models for comprehending paragraphs about processes (e.g., photosynthesis), an important genre of text describing a dynamic world. The new dataset, ProPara, is the first to contain natural (rather than machine-generated) text about a changing world along with a full annotation of entity states (location and existence) during those changes (81k datapoints). The end-task, tracking the location and existence of entities through the text, is challenging because the causal effects of actions are often implicit and need to be inferred. We find that previous models that have worked well on synthetic data achieve only mediocre performance on ProPara, and introduce two new neural models that exploit alternative mechanisms for state prediction, in particular using LSTM input encoding and span prediction. The new models improve accuracy by up to 19%. We are releasing the ProPara dataset and our models to the community.

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Natural Language to Structured Query Generation via Meta-Learning
Po-Sen Huang | Chenglong Wang | Rishabh Singh | Wen-tau Yih | Xiaodong He
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

In conventional supervised training, a model is trained to fit all the training examples. However, having a monolithic model may not always be the best strategy, as examples could vary widely. In this work, we explore a different learning protocol that treats each example as a unique pseudo-task, by reducing the original learning problem to a few-shot meta-learning scenario with the help of a domain-dependent relevance function. When evaluated on the WikiSQL dataset, our approach leads to faster convergence and achieves 1.1%–5.4% absolute accuracy gains over the non-meta-learning counterparts.

2017

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Maximum Margin Reward Networks for Learning from Explicit and Implicit Supervision
Haoruo Peng | Ming-Wei Chang | Wen-tau Yih
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Neural networks have achieved state-of-the-art performance on several structured-output prediction tasks, trained in a fully supervised fashion. However, annotated examples in structured domains are often costly to obtain, which thus limits the applications of neural networks. In this work, we propose Maximum Margin Reward Networks, a neural network-based framework that aims to learn from both explicit (full structures) and implicit supervision signals (delayed feedback on the correctness of the predicted structure). On named entity recognition and semantic parsing, our model outperforms previous systems on the benchmark datasets, CoNLL-2003 and WebQuestionsSP.

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Search-based Neural Structured Learning for Sequential Question Answering
Mohit Iyyer | Wen-tau Yih | Ming-Wei Chang
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Recent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. In an effort to explore a conversational QA setting, we present a more realistic task: answering sequences of simple but inter-related questions. We collect a dataset of 6,066 question sequences that inquire about semi-structured tables from Wikipedia, with 17,553 question-answer pairs in total. To solve this sequential question answering task, we propose a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search. Our model effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions.

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NLP for Precision Medicine
Hoifung Poon | Chris Quirk | Kristina Toutanova | Wen-tau Yih
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts

We will introduce precision medicine and showcase the vast opportunities for NLP in this burgeoning field with great societal impact. We will review pressing NLP problems, state-of-the art methods, and important applications, as well as datasets, medical resources, and practical issues. The tutorial will provide an accessible overview of biomedicine, and does not presume knowledge in biology or healthcare. The ultimate goal is to reduce the entry barrier for NLP researchers to contribute to this exciting domain.

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Cross-Sentence N-ary Relation Extraction with Graph LSTMs
Nanyun Peng | Hoifung Poon | Chris Quirk | Kristina Toutanova | Wen-tau Yih
Transactions of the Association for Computational Linguistics, Volume 5

Past work in relation extraction has focused on binary relations in single sentences. Recent NLP inroads in high-value domains have sparked interest in the more general setting of extracting n-ary relations that span multiple sentences. In this paper, we explore a general relation extraction framework based on graph long short-term memory networks (graph LSTMs) that can be easily extended to cross-sentence n-ary relation extraction. The graph formulation provides a unified way of exploring different LSTM approaches and incorporating various intra-sentential and inter-sentential dependencies, such as sequential, syntactic, and discourse relations. A robust contextual representation is learned for the entities, which serves as input to the relation classifier. This simplifies handling of relations with arbitrary arity, and enables multi-task learning with related relations. We evaluate this framework in two important precision medicine settings, demonstrating its effectiveness with both conventional supervised learning and distant supervision. Cross-sentence extraction produced larger knowledge bases. and multi-task learning significantly improved extraction accuracy. A thorough analysis of various LSTM approaches yielded useful insight the impact of linguistic analysis on extraction accuracy.

2016

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Question Answering with Knowledge Base, Web and Beyond
Wen-tau Yih | Hao Ma
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts

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Proceedings of the 1st Workshop on Representation Learning for NLP
Phil Blunsom | Kyunghyun Cho | Shay Cohen | Edward Grefenstette | Karl Moritz Hermann | Laura Rimell | Jason Weston | Scott Wen-tau Yih
Proceedings of the 1st Workshop on Representation Learning for NLP

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Story Cloze Evaluator: Vector Space Representation Evaluation by Predicting What Happens Next
Nasrin Mostafazadeh | Lucy Vanderwende | Wen-tau Yih | Pushmeet Kohli | James Allen
Proceedings of the 1st Workshop on Evaluating Vector-Space Representations for NLP

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Learning from Explicit and Implicit Supervision Jointly For Algebra Word Problems
Shyam Upadhyay | Ming-Wei Chang | Kai-Wei Chang | Wen-tau Yih
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Compositional Learning of Embeddings for Relation Paths in Knowledge Base and Text
Kristina Toutanova | Xi Victoria Lin | Wen-tau Yih | Hoifung Poon | Chris Quirk
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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The Value of Semantic Parse Labeling for Knowledge Base Question Answering
Wen-tau Yih | Matthew Richardson | Chris Meek | Ming-Wei Chang | Jina Suh
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2015

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WikiQA: A Challenge Dataset for Open-Domain Question Answering
Yi Yang | Wen-tau Yih | Christopher Meek
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Deep Learning and Continuous Representations for Natural Language Processing
Wen-tau Yih | Xiaodong He | Jianfeng Gao
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorial Abstracts

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Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality
Alexandre Allauzen | Edward Grefenstette | Karl Moritz Hermann | Hugo Larochelle | Scott Wen-tau Yih
Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality

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Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base
Wen-tau Yih | Ming-Wei Chang | Xiaodong He | Jianfeng Gao
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

2014

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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)

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Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)
Alexandre Allauzen | Raffaella Bernardi | Edward Grefenstette | Hugo Larochelle | Christopher Manning | Scott Wen-tau Yih
Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)

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Proceedings of the Eighteenth Conference on Computational Natural Language Learning
Roser Morante | Scott Wen-tau Yih
Proceedings of the Eighteenth Conference on Computational Natural Language Learning

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Learning Continuous Phrase Representations for Translation Modeling
Jianfeng Gao | Xiaodong He | Wen-tau Yih | Li Deng
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Semantic Parsing for Single-Relation Question Answering
Wen-tau Yih | Xiaodong He | Christopher Meek
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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Linguistic Regularities in Continuous Space Word Representations
Tomas Mikolov | Wen-tau Yih | Geoffrey Zweig
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Combining Heterogeneous Models for Measuring Relational Similarity
Alisa Zhila | Wen-tau Yih | Christopher Meek | Geoffrey Zweig | Tomas Mikolov
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Question Answering Using Enhanced Lexical Semantic Models
Wen-tau Yih | Ming-Wei Chang | Christopher Meek | Andrzej Pastusiak
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Dual Coordinate Descent Algorithms for Efficient Large Margin Structured Prediction
Ming-Wei Chang | Wen-tau Yih
Transactions of the Association for Computational Linguistics, Volume 1

Due to the nature of complex NLP problems, structured prediction algorithms have been important modeling tools for a wide range of tasks. While there exists evidence showing that linear Structural Support Vector Machine (SSVM) algorithm performs better than structured Perceptron, the SSVM algorithm is still less frequently chosen in the NLP community because of its relatively slow training speed. In this paper, we propose a fast and easy-to-implement dual coordinate descent algorithm for SSVMs. Unlike algorithms such as Perceptron and stochastic gradient descent, our method keeps track of dual variables and updates the weight vector more aggressively. As a result, this training process is as efficient as existing online learning methods, and yet derives consistently better models, as evaluated on four benchmark NLP datasets for part-of-speech tagging, named-entity recognition and dependency parsing.

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Animacy Detection with Voting Models
Joshua Moore | Christopher J.C. Burges | Erin Renshaw | Wen-tau Yih
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Multi-Relational Latent Semantic Analysis
Kai-Wei Chang | Wen-tau Yih | Christopher Meek
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

2012

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Polarity Inducing Latent Semantic Analysis
Wen-tau Yih | Geoffrey Zweig | John Platt
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning

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Measuring Word Relatedness Using Heterogeneous Vector Space Models
Wen-tau Yih | Vahed Qazvinian
Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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MSR SPLAT, a language analysis toolkit
Chris Quirk | Pallavi Choudhury | Jianfeng Gao | Hisami Suzuki | Kristina Toutanova | Michael Gamon | Wen-tau Yih | Colin Cherry | Lucy Vanderwende
Proceedings of the Demonstration Session at the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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Learning Discriminative Projections for Text Similarity Measures
Wen-tau Yih | Kristina Toutanova | John C. Platt | Christopher Meek
Proceedings of the Fifteenth Conference on Computational Natural Language Learning

2010

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Translingual Document Representations from Discriminative Projections
John Platt | Kristina Toutanova | Wen-tau Yih
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

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Learning Term-weighting Functions for Similarity Measures
Wen-tau Yih
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

2008

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The Importance of Syntactic Parsing and Inference in Semantic Role Labeling
Vasin Punyakanok | Dan Roth | Wen-tau Yih
Computational Linguistics, Volume 34, Number 2, June 2008 - Special Issue on Semantic Role Labeling

2006

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Improved Discriminative Bilingual Word Alignment
Robert C. Moore | Wen-tau Yih | Andreas Bode
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

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Automatic Semantic Role Labeling
Scott Wen-tau Yih | Kristina Toutanova
Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Tutorial Abstracts

2005

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Generalized Inference with Multiple Semantic Role Labeling Systems
Peter Koomen | Vasin Punyakanok | Dan Roth | Wen-tau Yih
Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005)

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Demonstrating an Interactive Semantic Role Labeling System
Vasin Punyakanok | Dan Roth | Mark Sammons | Wen-tau Yih
Proceedings of HLT/EMNLP 2005 Interactive Demonstrations

2004

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Semantic Role Labeling Via Integer Linear Programming Inference
Vasin Punyakanok | Dan Roth | Wen-tau Yih | Dav Zimak
COLING 2004: Proceedings of the 20th International Conference on Computational Linguistics

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A Linear Programming Formulation for Global Inference in Natural Language Tasks
Dan Roth | Wen-tau Yih
Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004

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Semantic Role Labeling Via Generalized Inference Over Classifiers
Vasin Punyakanok | Dan Roth | Wen-tau Yih | Dav Zimak | Yuancheng Tu
Proceedings of the Eighth Conference on Computational Natural Language Learning (CoNLL-2004) at HLT-NAACL 2004

2002

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Probabilistic Reasoning for Entity & Relation Recognition
Dan Roth | Wen-tau Yih
COLING 2002: The 19th International Conference on Computational Linguistics

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