Tao Lei


2021

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Nutri-bullets Hybrid: Consensual Multi-document Summarization
Darsh Shah | Lili Yu | Tao Lei | Regina Barzilay
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We present a method for generating comparative summaries that highlight similarities and contradictions in input documents. The key challenge in creating such summaries is the lack of large parallel training data required for training typical summarization systems. To this end, we introduce a hybrid generation approach inspired by traditional concept-to-text systems. To enable accurate comparison between different sources, the model first learns to extract pertinent relations from input documents. The content planning component uses deterministic operators to aggregate these relations after identifying a subset for inclusion into a summary. The surface realization component lexicalizes this information using a text-infilling language model. By separately modeling content selection and realization, we can effectively train them with limited annotations. We implemented and tested the model in the domain of nutrition and health – rife with inconsistencies. Compared to conventional methods, our framework leads to more faithful, relevant and aggregation-sensitive summarization – while being equally fluent.

2020

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Interactive Classification by Asking Informative Questions
Lili Yu | Howard Chen | Sida I. Wang | Tao Lei | Yoav Artzi
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We study the potential for interaction in natural language classification. We add a limited form of interaction for intent classification, where users provide an initial query using natural language, and the system asks for additional information using binary or multi-choice questions. At each turn, our system decides between asking the most informative question or making the final classification pre-diction. The simplicity of the model allows for bootstrapping of the system without interaction data, instead relying on simple crowd-sourcing tasks. We evaluate our approach on two domains, showing the benefit of interaction and the advantage of learning to balance between asking additional questions and making the final prediction.

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Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport
Kyle Swanson | Lili Yu | Tao Lei
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Selecting input features of top relevance has become a popular method for building self-explaining models. In this work, we extend this selective rationalization approach to text matching, where the goal is to jointly select and align text pieces, such as tokens or sentences, as a justification for the downstream prediction. Our approach employs optimal transport (OT) to find a minimal cost alignment between the inputs. However, directly applying OT often produces dense and therefore uninterpretable alignments. To overcome this limitation, we introduce novel constrained variants of the OT problem that result in highly sparse alignments with controllable sparsity. Our model is end-to-end differentiable using the Sinkhorn algorithm for OT and can be trained without any alignment annotations. We evaluate our model on the StackExchange, MultiNews, e-SNLI, and MultiRC datasets. Our model achieves very sparse rationale selections with high fidelity while preserving prediction accuracy compared to strong attention baseline models.

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Autoregressive Knowledge Distillation through Imitation Learning
Alexander Lin | Jeremy Wohlwend | Howard Chen | Tao Lei
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The performance of autoregressive models on natural language generation tasks has dramatically improved due to the adoption of deep, self-attentive architectures. However, these gains have come at the cost of hindering inference speed, making state-of-the-art models cumbersome to deploy in real-world, time-sensitive settings. We develop a compression technique for autoregressive models that is driven by an imitation learning perspective on knowledge distillation. The algorithm is designed to address the exposure bias problem. On prototypical language generation tasks such as translation and summarization, our method consistently outperforms other distillation algorithms, such as sequence-level knowledge distillation. Student models trained with our method attain 1.4 to 4.8 BLEU/ROUGE points higher than those trained from scratch, while increasing inference speed by up to 14 times in comparison to the teacher model.

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Structured Pruning of Large Language Models
Ziheng Wang | Jeremy Wohlwend | Tao Lei
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Large language models have recently achieved state of the art performance across a wide variety of natural language tasks. Meanwhile, the size of these models and their latency have significantly increased, which makes their usage costly, and raises an interesting question: do language models need to be large? We study this question through the lens of model compression. We present a generic, structured pruning approach by parameterizing each weight matrix using its low-rank factorization, and adaptively removing rank-1 components during training. On language modeling tasks, our structured approach outperforms other unstructured and block-structured pruning baselines at various compression levels, while achieving significant speedups during both training and inference. We also demonstrate that our method can be applied to pruning adaptive word embeddings in large language models, and to pruning the BERT model on several downstream fine-tuning classification benchmarks.

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面向垂直领域的阅读理解数据增强方法(Method for reading comprehension data enhancement in vertical field)
Zhengwei Lv (吕政伟) | Lei Yang (杨雷) | Zhizhong Shi (石智中) | Xiao Liang (梁霄) | Tao Lei (雷涛) | Duoxing Liu (刘多星)
Proceedings of the 19th Chinese National Conference on Computational Linguistics

阅读理解问答系统是利用语义理解等自然语言处理技术,根据输入问题,对非结构化文档数据进行分析,生成一个答案,具有很高的研究和应用价值。在垂直领域应用过程中,阅读理解问答数据标注成本高且用户问题表达复杂多样,使得阅读理解问答系统准确率低、鲁棒性差。针对这一问题,本文提出一种面向垂直领域的阅读理解问答数据的增强方法,该方法基于真实用户问题,构造阅读理解训练数据,一方面降低标注成本,另一方面增加训练数据多样性,提升模型的准确率和鲁棒性。本文用汽车领域数据对该方法进行实验验证,其结果表明该方法对垂直领域阅读理解模型的准确率和鲁棒性均能有效提升。

2019

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Metric Learning for Dynamic Text Classification
Jeremy Wohlwend | Ethan R. Elenberg | Sam Altschul | Shawn Henry | Tao Lei
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

Traditional text classifiers are limited to predicting over a fixed set of labels. However, in many real-world applications the label set is frequently changing. For example, in intent classification, new intents may be added over time while others are removed. We propose to address the problem of dynamic text classification by replacing the traditional, fixed-size output layer with a learned, semantically meaningful metric space. Here the distances between textual inputs are optimized to perform nearest-neighbor classification across overlapping label sets. Changing the label set does not involve removing parameters, but rather simply adding or removing support points in the metric space. Then the learned metric can be fine-tuned with only a few additional training examples. We demonstrate that this simple strategy is robust to changes in the label space. Furthermore, our results show that learning a non-Euclidean metric can improve performance in the low data regime, suggesting that further work on metric spaces may benefit low-resource research.

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Building a Production Model for Retrieval-Based Chatbots
Kyle Swanson | Lili Yu | Christopher Fox | Jeremy Wohlwend | Tao Lei
Proceedings of the First Workshop on NLP for Conversational AI

Response suggestion is an important task for building human-computer conversation systems. Recent approaches to conversation modeling have introduced new model architectures with impressive results, but relatively little attention has been paid to whether these models would be practical in a production setting. In this paper, we describe the unique challenges of building a production retrieval-based conversation system, which selects outputs from a whitelist of candidate responses. To address these challenges, we propose a dual encoder architecture which performs rapid inference and scales well with the size of the whitelist. We also introduce and compare two methods for generating whitelists, and we carry out a comprehensive analysis of the model and whitelists. Experimental results on a large, proprietary help desk chat dataset, including both offline metrics and a human evaluation, indicate production-quality performance and illustrate key lessons about conversation modeling in practice.

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AUTOHOME-ORCA at SemEval-2019 Task 8: Application of BERT for Fact-Checking in Community Forums
Zhengwei Lv | Duoxing Liu | Haifeng Sun | Xiao Liang | Tao Lei | Zhizhong Shi | Feng Zhu | Lei Yang
Proceedings of the 13th International Workshop on Semantic Evaluation

Fact checking is an important task for maintaining high quality posts and improving user experience in Community Question Answering forums. Therefore, the SemEval-2019 task 8 is aimed to identify factual question (subtask A) and detect true factual information from corresponding answers (subtask B). In order to address this task, we propose a system based on the BERT model with meta information of questions. For the subtask A, the outputs of fine-tuned BERT classification model are combined with the feature of length of questions to boost the performance. For the subtask B, the predictions of several variants of BERT model encoding the meta information are combined to create an ensemble model. Our system achieved competitive results with an accuracy of 0.82 in the subtask A and 0.83 in the subtask B. The experimental results validate the effectiveness of our system.

2018

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Adversarial Domain Adaptation for Duplicate Question Detection
Darsh Shah | Tao Lei | Alessandro Moschitti | Salvatore Romeo | Preslav Nakov
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6% over the best baseline across multiple pairs of domains.

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Simple Recurrent Units for Highly Parallelizable Recurrence
Tao Lei | Yu Zhang | Sida I. Wang | Hui Dai | Yoav Artzi
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Common recurrent neural architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU), a light recurrent unit that balances model capacity and scalability. SRU is designed to provide expressive recurrence, enable highly parallelized implementation, and comes with careful initialization to facilitate training of deep models. We demonstrate the effectiveness of SRU on multiple NLP tasks. SRU achieves 5—9x speed-up over cuDNN-optimized LSTM on classification and question answering datasets, and delivers stronger results than LSTM and convolutional models. We also obtain an average of 0.7 BLEU improvement over the Transformer model (Vaswani et al., 2017) on translation by incorporating SRU into the architecture.

2016

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Rationalizing Neural Predictions
Tao Lei | Regina Barzilay | Tommi Jaakkola
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Learning to refine text based recommendations
Youyang Gu | Tao Lei | Regina Barzilay | Tommi Jaakkola
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Proceedings of the ACL 2016 Student Research Workshop
He He | Tao Lei | Will Roberts
Proceedings of the ACL 2016 Student Research Workshop

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Making Dependency Labeling Simple, Fast and Accurate
Tianxiao Shen | Tao Lei | Regina Barzilay
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Semi-supervised Question Retrieval with Gated Convolutions
Tao Lei | Hrishikesh Joshi | Regina Barzilay | Tommi Jaakkola | Kateryna Tymoshenko | Alessandro Moschitti | Lluís Màrquez
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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SLS at SemEval-2016 Task 3: Neural-based Approaches for Ranking in Community Question Answering
Mitra Mohtarami | Yonatan Belinkov | Wei-Ning Hsu | Yu Zhang | Tao Lei | Kfir Bar | Scott Cyphers | Jim Glass
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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Molding CNNs for text: non-linear, non-consecutive convolutions
Tao Lei | Regina Barzilay | Tommi Jaakkola
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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High-Order Low-Rank Tensors for Semantic Role Labeling
Tao Lei | Yuan Zhang | Lluís Màrquez | Alessandro Moschitti | Regina Barzilay
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment
Yonatan Belinkov | Tao Lei | Regina Barzilay | Amir Globerson
Transactions of the Association for Computational Linguistics, Volume 2

Prepositional phrase (PP) attachment disambiguation is a known challenge in syntactic parsing. The lexical sparsity associated with PP attachments motivates research in word representations that can capture pertinent syntactic and semantic features of the word. One promising solution is to use word vectors induced from large amounts of raw text. However, state-of-the-art systems that employ such representations yield modest gains in PP attachment accuracy. In this paper, we show that word vector representations can yield significant PP attachment performance gains. This is achieved via a non-linear architecture that is discriminatively trained to maximize PP attachment accuracy. The architecture is initialized with word vectors trained from unlabeled data, and relearns those to maximize attachment accuracy. We obtain additional performance gains with alternative representations such as dependency-based word vectors. When tested on both English and Arabic datasets, our method outperforms both a strong SVM classifier and state-of-the-art parsers. For instance, we achieve 82.6% PP attachment accuracy on Arabic, while the Turbo and Charniak self-trained parsers obtain 76.7% and 80.8% respectively.

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Greed is Good if Randomized: New Inference for Dependency Parsing
Yuan Zhang | Tao Lei | Regina Barzilay | Tommi Jaakkola
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Steps to Excellence: Simple Inference with Refined Scoring of Dependency Trees
Yuan Zhang | Tao Lei | Regina Barzilay | Tommi Jaakkola | Amir Globerson
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Low-Rank Tensors for Scoring Dependency Structures
Tao Lei | Yu Xin | Yuan Zhang | Regina Barzilay | Tommi Jaakkola
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2013

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From Natural Language Specifications to Program Input Parsers
Tao Lei | Fan Long | Regina Barzilay | Martin Rinard
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

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Learning High-Level Planning from Text
S.R.K. Branavan | Nate Kushman | Tao Lei | Regina Barzilay
Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)