Yi Yang


2021

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Constructing a Psychometric Testbed for Fair Natural Language Processing
Ahmed Abbasi | David Dobolyi | John P. Lalor | Richard G. Netemeyer | Kendall Smith | Yi Yang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Psychometric measures of ability, attitudes, perceptions, and beliefs are crucial for understanding user behavior in various contexts including health, security, e-commerce, and finance. Traditionally, psychometric dimensions have been measured and collected using survey-based methods. Inferring such constructs from user-generated text could allow timely, unobtrusive collection and analysis. In this paper we describe our efforts to construct a corpus for psychometric natural language processing (NLP) related to important dimensions such as trust, anxiety, numeracy, and literacy, in the health domain. We discuss our multi-step process to align user text with their survey-based response items and provide an overview of the resulting testbed which encompasses survey-based psychometric measures and accompanying user-generated text from 8,502 respondents. Our testbed also encompasses self-reported demographic information, including race, sex, age, income, and education - thereby affording opportunities for measuring bias and benchmarking fairness of text classification methods. We report preliminary results on use of the text to predict/categorize users’ survey response labels - and on the fairness of these models. We also discuss the important implications of our work and resulting testbed for future NLP research on psychometrics and fairness.

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Learning Numeracy: A Simple Yet Effective Number Embedding Approach Using Knowledge Graph
Hanyu Duan | Yi Yang | Kar Yan Tam
Findings of the Association for Computational Linguistics: EMNLP 2021

Numeracy plays a key role in natural language understanding. However, existing NLP approaches, not only traditional word2vec approach or contextualized transformer-based language models, fail to learn numeracy. As the result, the performance of these models is limited when they are applied to number-intensive applications in clinical and financial domains. In this work, we propose a simple number embedding approach based on knowledge graph. We construct a knowledge graph consisting of number entities and magnitude relations. Knowledge graph embedding method is then applied to obtain number vectors. Our approach is easy to implement, and experiment results on various numeracy-related NLP tasks demonstrate the effectiveness and efficiency of our method.

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Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems
Derek Chen | Howard Chen | Yi Yang | Alexander Lin | Zhou Yu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Existing goal-oriented dialogue datasets focus mainly on identifying slots and values. However, customer support interactions in reality often involve agents following multi-step procedures derived from explicitly-defined company policies as well. To study customer service dialogue systems in more realistic settings, we introduce the Action-Based Conversations Dataset (ABCD), a fully-labeled dataset with over 10K human-to-human dialogues containing 55 distinct user intents requiring unique sequences of actions constrained by policies to achieve task success. We propose two additional dialog tasks, Action State Tracking and Cascading Dialogue Success, and establish a series of baselines involving large-scale, pre-trained language models on this dataset. Empirical results demonstrate that while more sophisticated networks outperform simpler models, a considerable gap (50.8% absolute accuracy) still exists to reach human-level performance on ABCD.

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CLIP: A Dataset for Extracting Action Items for Physicians from Hospital Discharge Notes
James Mullenbach | Yada Pruksachatkun | Sean Adler | Jennifer Seale | Jordan Swartz | Greg McKelvey | Hui Dai | Yi Yang | David Sontag
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)

Continuity of care is crucial to ensuring positive health outcomes for patients discharged from an inpatient hospital setting, and improved information sharing can help. To share information, caregivers write discharge notes containing action items to share with patients and their future caregivers, but these action items are easily lost due to the lengthiness of the documents. In this work, we describe our creation of a dataset of clinical action items annotated over MIMIC-III, the largest publicly available dataset of real clinical notes. This dataset, which we call CLIP, is annotated by physicians and covers 718 documents representing 100K sentences. We describe the task of extracting the action items from these documents as multi-aspect extractive summarization, with each aspect representing a type of action to be taken. We evaluate several machine learning models on this task, and show that the best models exploit in-domain language model pre-training on 59K unannotated documents, and incorporate context from neighboring sentences. We also propose an approach to pre-training data selection that allows us to explore the trade-off between size and domain-specificity of pre-training datasets for this task.

2020

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Interpretable Operational Risk Classification with Semi-Supervised Variational Autoencoder
Fan Zhou | Shengming Zhang | Yi Yang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Operational risk management is one of the biggest challenges nowadays faced by financial institutions. There are several major challenges of building a text classification system for automatic operational risk prediction, including imbalanced labeled/unlabeled data and lacking interpretability. To tackle these challenges, we present a semi-supervised text classification framework that integrates multi-head attention mechanism with Semi-supervised variational inference for Operational Risk Classification (SemiORC). We empirically evaluate the framework on a real-world dataset. The results demonstrate that our method can better utilize unlabeled data and learn visually interpretable document representations. SemiORC also outperforms other baseline methods on operational risk classification.

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Interpreting Twitter User Geolocation
Ting Zhong | Tianliang Wang | Fan Zhou | Goce Trajcevski | Kunpeng Zhang | Yi Yang
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Identifying user geolocation in online social networks is an essential task in many location-based applications. Existing methods rely on the similarity of text and network structure, however, they suffer from a lack of interpretability on the corresponding results, which is crucial for understanding model behavior. In this work, we adopt influence functions to interpret the behavior of GNN-based models by identifying the importance of training users when predicting the locations of the testing users. This methodology helps with providing meaningful explanations on prediction results. Furthermore, it also initiates an attempt to uncover the so-called “black-box” GNN-based models by investigating the effect of individual nodes.

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Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification
Linyi Yang | Eoin Kenny | Tin Lok James Ng | Yi Yang | Barry Smyth | Ruihai Dong
Proceedings of the 28th International Conference on Computational Linguistics

Corporate mergers and acquisitions (M&A) account for billions of dollars of investment globally every year and offer an interesting and challenging domain for artificial intelligence. However, in these highly sensitive domains, it is crucial to not only have a highly robust/accurate model, but be able to generate useful explanations to garner a user’s trust in the automated system. Regrettably, the recent research regarding eXplainable AI (XAI) in financial text classification has received little to no attention, and many current methods for generating textual-based explanations result in highly implausible explanations, which damage a user’s trust in the system. To address these issues, this paper proposes a novel methodology for producing plausible counterfactual explanations, whilst exploring the regularization benefits of adversarial training on language models in the domain of FinTech. Exhaustive quantitative experiments demonstrate that not only does this approach improve the model accuracy when compared to the current state-of-the-art and human performance, but it also generates counterfactual explanations which are significantly more plausible based on human trials.

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Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning
Yi Yang | Arzoo Katiyar
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We present a simple few-shot named entity recognition (NER) system based on nearest neighbor learning and structured inference. Our system uses a supervised NER model trained on the source domain, as a feature extractor. Across several test domains, we show that a nearest neighbor classifier in this feature-space is far more effective than the standard meta-learning approaches. We further propose a cheap but effective method to capture the label dependencies between entity tags without expensive CRF training. We show that our method of combining structured decoding with nearest neighbor learning achieves state-of-the-art performance on standard few-shot NER evaluation tasks, improving F1 scores by 6% to 16% absolute points over prior meta-learning based systems.

2019

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Dialog Intent Induction with Deep Multi-View Clustering
Hugh Perkins | Yi Yang
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

We introduce the dialog intent induction task and present a novel deep multi-view clustering approach to tackle the problem. Dialog intent induction aims at discovering user intents from user query utterances in human-human conversations such as dialogs between customer support agents and customers. Motivated by the intuition that a dialog intent is not only expressed in the user query utterance but also captured in the rest of the dialog, we split a conversation into two independent views and exploit multi-view clustering techniques for inducing the dialog intent. In par- ticular, we propose alternating-view k-means (AV-KMEANS) for joint multi-view represen- tation learning and clustering analysis. The key innovation is that the instance-view representations are updated iteratively by predicting the cluster assignment obtained from the alternative view, so that the multi-view representations of the instances lead to similar cluster assignments. Experiments on two public datasets show that AV-KMEANS can induce better dialog intent clusters than state-of-the-art unsupervised representation learning methods and standard multi-view clustering approaches.

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What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues
Yu Qin | Yi Yang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Predicting financial risk is an essential task in financial market. Prior research has shown that textual information in a firm’s financial statement can be used to predict its stock’s risk level. Nowadays, firm CEOs communicate information not only verbally through press releases and financial reports, but also nonverbally through investor meetings and earnings conference calls. There are anecdotal evidences that CEO’s vocal features, such as emotions and voice tones, can reveal the firm’s performance. However, how vocal features can be used to predict risk levels, and to what extent, is still unknown. To fill the gap, we obtain earnings call audio recordings and textual transcripts for S&P 500 companies in recent years. We propose a multimodal deep regression model (MDRM) that jointly model CEO’s verbal (from text) and vocal (from audio) information in a conference call. Empirical results show that our model that jointly considers verbal and vocal features achieves significant and substantial prediction error reduction. We also discuss several interesting findings and the implications to financial markets. The processed earnings conference calls data (text and audio) are released for readers who are interested in reproducing the results or designing trading strategy.

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Syntax-Infused Variational Autoencoder for Text Generation
Xinyuan Zhang | Yi Yang | Siyang Yuan | Dinghan Shen | Lawrence Carin
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We present a syntax-infused variational autoencoder (SIVAE), that integrates sentences with their syntactic trees to improve the grammar of generated sentences. Distinct from existing VAE-based text generative models, SIVAE contains two separate latent spaces, for sentences and syntactic trees. The evidence lower bound objective is redesigned correspondingly, by optimizing a joint distribution that accommodates two encoders and two decoders. SIVAE works with long short-term memory architectures to simultaneously generate sentences and syntactic trees. Two versions of SIVAE are proposed: one captures the dependencies between the latent variables through a conditional prior network, and the other treats the latent variables independently such that syntactically-controlled sentence generation can be performed. Experimental results demonstrate the generative superiority of SIVAE on both reconstruction and targeted syntactic evaluations. Finally, we show that the proposed models can be used for unsupervised paraphrasing given different syntactic tree templates.

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A Semi-Markov Structured Support Vector Machine Model for High-Precision Named Entity Recognition
Ravneet Arora | Chen-Tse Tsai | Ketevan Tsereteli | Prabhanjan Kambadur | Yi Yang
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Named entity recognition (NER) is the backbone of many NLP solutions. F1 score, the harmonic mean of precision and recall, is often used to select/evaluate the best models. However, when precision needs to be prioritized over recall, a state-of-the-art model might not be the best choice. There is little in literature that directly addresses training-time modifications to achieve higher precision information extraction. In this paper, we propose a neural semi-Markov structured support vector machine model that controls the precision-recall trade-off by assigning weights to different types of errors in the loss-augmented inference during training. The semi-Markov property provides more accurate phrase-level predictions, thereby improving performance. We empirically demonstrate the advantage of our model when high precision is required by comparing against strong baselines based on CRF. In our experiments with the CoNLL 2003 dataset, our model achieves a better precision-recall trade-off at various precision levels.

2018

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Convolutional Neural Networks with Recurrent Neural Filters
Yi Yang
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We introduce a class of convolutional neural networks (CNNs) that utilize recurrent neural networks (RNNs) as convolution filters. A convolution filter is typically implemented as a linear affine transformation followed by a non-linear function, which fails to account for language compositionality. As a result, it limits the use of high-order filters that are often warranted for natural language processing tasks. In this work, we model convolution filters with RNNs that naturally capture compositionality and long-term dependencies in language. We show that simple CNN architectures equipped with recurrent neural filters (RNFs) achieve results that are on par with the best published ones on the Stanford Sentiment Treebank and two answer sentence selection datasets.

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Collective Entity Disambiguation with Structured Gradient Tree Boosting
Yi Yang | Ozan Irsoy | Kazi Shefaet Rahman
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 gradient-tree-boosting-based structured learning model for jointly disambiguating named entities in a document. Gradient tree boosting is a widely used machine learning algorithm that underlies many top-performing natural language processing systems. Surprisingly, most works limit the use of gradient tree boosting as a tool for regular classification or regression problems, despite the structured nature of language. To the best of our knowledge, our work is the first one that employs the structured gradient tree boosting (SGTB) algorithm for collective entity disambiguation. By defining global features over previous disambiguation decisions and jointly modeling them with local features, our system is able to produce globally optimized entity assignments for mentions in a document. Exact inference is prohibitively expensive for our globally normalized model. To solve this problem, we propose Bidirectional Beam Search with Gold path (BiBSG), an approximate inference algorithm that is a variant of the standard beam search algorithm. BiBSG makes use of global information from both past and future to perform better local search. Experiments on standard benchmark datasets show that SGTB significantly improves upon published results. Specifically, SGTB outperforms the previous state-of-the-art neural system by near 1% absolute accuracy on the popular AIDA-CoNLL dataset.

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Improve Neural Entity Recognition via Multi-Task Data Selection and Constrained Decoding
Huasha Zhao | Yi Yang | Qiong Zhang | Luo Si
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Entity recognition is a widely benchmarked task in natural language processing due to its massive applications. The state-of-the-art solution applies a neural architecture named BiLSTM-CRF to model the language sequences. In this paper, we propose an entity recognition system that improves this neural architecture with two novel techniques. The first technique is Multi-Task Data Selection, which ensures the consistency of data distribution and labeling guidelines between source and target datasets. The other one is constrained decoding using knowledge base. The decoder of the model operates at the document level, and leverages global and external information sources to further improve performance. Extensive experiments have been conducted to show the advantages of each technique. Our system achieves state-of-the-art results on the English entity recognition task in KBP 2017 official evaluation, and it also yields very strong results in other languages.

2017

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Alibaba at IJCNLP-2017 Task 1: Embedding Grammatical Features into LSTMs for Chinese Grammatical Error Diagnosis Task
Yi Yang | Pengjun Xie | Jun Tao | Guangwei Xu | Linlin Li | Luo Si
Proceedings of the IJCNLP 2017, Shared Tasks

This paper introduces Alibaba NLP team system on IJCNLP 2017 shared task No. 1 Chinese Grammatical Error Diagnosis (CGED). The task is to diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W). We treat the task as a sequence tagging problem and design some handcraft features to solve it. Our system is mainly based on the LSTM-CRF model and 3 ensemble strategies are applied to improve the performance. At the identification level and the position level our system gets the highest F1 scores. At the position level, which is the most difficult level, we perform best on all metrics.

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Overcoming Language Variation in Sentiment Analysis with Social Attention
Yi Yang | Jacob Eisenstein
Transactions of the Association for Computational Linguistics, Volume 5

Variation in language is ubiquitous, particularly in newer forms of writing such as social media. Fortunately, variation is not random; it is often linked to social properties of the author. In this paper, we show how to exploit social networks to make sentiment analysis more robust to social language variation. The key idea is linguistic homophily: the tendency of socially linked individuals to use language in similar ways. We formalize this idea in a novel attention-based neural network architecture, in which attention is divided among several basis models, depending on the author’s position in the social network. This has the effect of smoothing the classification function across the social network, and makes it possible to induce personalized classifiers even for authors for whom there is no labeled data or demographic metadata. This model significantly improves the accuracies of sentiment analysis on Twitter and on review data.

2016

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Toward Socially-Infused Information Extraction: Embedding Authors, Mentions, and Entities
Yi Yang | Ming-Wei Chang | Jacob Eisenstein
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

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Part-of-Speech Tagging for Historical English
Yi Yang | Jacob Eisenstein
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Efficient Methods for Incorporating Knowledge into Topic Models
Yi Yang | Doug Downey | Jordan Boyd-Graber
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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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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Unsupervised Multi-Domain Adaptation with Feature Embeddings
Yi Yang | Jacob Eisenstein
Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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S-MART: Novel Tree-based Structured Learning Algorithms Applied to Tweet Entity Linking
Yi Yang | Ming-Wei Chang
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)

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Efficient Methods for Inferring Large Sparse Topic Hierarchies
Doug Downey | Chandra Bhagavatula | Yi Yang
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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Active Learning with Constrained Topic Model
Yi Yang | Shimei Pan | Doug Downey | Kunpeng Zhang
Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces

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Learning Representations for Weakly Supervised Natural Language Processing Tasks
Fei Huang | Arun Ahuja | Doug Downey | Yi Yang | Yuhong Guo | Alexander Yates
Computational Linguistics, Volume 40, Issue 1 - March 2014

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Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout
Yi Yang | Jacob Eisenstein
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2013

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A Log-Linear Model for Unsupervised Text Normalization
Yi Yang | Jacob Eisenstein
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

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Overcoming the Memory Bottleneck in Distributed Training of Latent Variable Models of Text
Yi Yang | Alexander Yates | Doug Downey
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2011

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Quality-biased Ranking of Short Texts in Microblogging Services
Minlie Huang | Yi Yang | Xiaoyan Zhu
Proceedings of 5th International Joint Conference on Natural Language Processing