Shyam Upadhyay


Efficient Encoders for Streaming Sequence Tagging
Ayush Kaushal | Aditya Gupta | Shyam Upadhyay | Manaal Faruqui
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

A naive application of state-of-the-art bidirectional encoders for streaming sequence tagging would require encoding each token from scratch for each new token in an incremental streaming input (like transcribed speech). The lack of re-usability of previous computation leads to a higher number of Floating Point Operations (or FLOPs) and higher number of unnecessary label flips. Increased FLOPs consequently lead to higher wall-clock time and increased label flipping leads to poorer streaming performance. In this work, we present a Hybrid Encoder with Adaptive Restart (HEAR) that addresses these issues while maintaining the performance of bidirectional encoders over the offline (or complete) and improving streaming (or incomplete) inputs. HEAR has a Hybrid unidirectional-bidirectional encoder architecture to perform sequence tagging, along with an Adaptive Restart Module (ARM) to selectively guide the restart of bidirectional portion of the encoder. Across four sequence tagging tasks, HEAR offers FLOP savings in streaming settings upto 71.1% and also outperforms bidirectional encoders for streaming predictions by upto +10% streaming exact match.


TableFormer: Robust Transformer Modeling for Table-Text Encoding
Jingfeng Yang | Aditya Gupta | Shyam Upadhyay | Luheng He | Rahul Goel | Shachi Paul
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Understanding tables is an important aspect of natural language understanding. Existing models for table understanding require linearization of the table structure, where row or column order is encoded as an unwanted bias. Such spurious biases make the model vulnerable to row and column order perturbations. Additionally, prior work has not thoroughly modeled the table structures or table-text alignments, hindering the table-text understanding ability. In this work, we propose a robust and structurally aware table-text encoding architecture TableFormer, where tabular structural biases are incorporated completely through learnable attention biases. TableFormer is (1) strictly invariant to row and column orders, and, (2) could understand tables better due to its tabular inductive biases. Our evaluations showed that TableFormer outperforms strong baselines in all settings on SQA, WTQ and TabFact table reasoning datasets, and achieves state-of-the-art performance on SQA, especially when facing answer-invariant row and column order perturbations (6% improvement over the best baseline), because previous SOTA models’ performance drops by 4% - 6% when facing such perturbations while TableFormer is not affected.


TIMEDIAL: Temporal Commonsense Reasoning in Dialog
Lianhui Qin | Aditya Gupta | Shyam Upadhyay | Luheng He | Yejin Choi | Manaal Faruqui
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)

Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progress with massive pre-trained language models (LMs) such as T5 and GPT-3, their capability of temporal reasoning in dialogs remains largely under-explored. In this paper, we present the first study to investigate pre-trained LMs for their temporal reasoning capabilities in dialogs by introducing a new task and a crowd-sourced English challenge set, TimeDial. We formulate TimeDial as a multiple choice cloze task with over 1.1K carefully curated dialogs. Empirical results demonstrate that even the best performing models struggle on this task compared to humans, with 23 absolute points of gap in accuracy. Furthermore, our analysis reveals that the models fail to reason about dialog context correctly; instead, they rely on shallow cues based on existing temporal patterns in context, motivating future research for modeling temporal concepts in text and robust contextual reasoning about them. The dataset is publicly available at

Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
Aditya Gupta | Jiacheng Xu | Shyam Upadhyay | Diyi Yang | Manaal Faruqui
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021


A General-Purpose Algorithm for Constrained Sequential Inference
Daniel Deutsch | Shyam Upadhyay | Dan Roth
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)

Inference in structured prediction involves finding the best output structure for an input, subject to certain constraints. Many current approaches use sequential inference, which constructs the output in a left-to-right manner. However, there is no general framework to specify constraints in these approaches. We present a principled approach for incorporating constraints into sequential inference algorithms. Our approach expresses constraints using an automaton, which is traversed in lock-step during inference, guiding the search to valid outputs. We show that automata can express commonly used constraints and are easily incorporated into sequential inference. When it is more natural to represent constraints as a set of automata, our algorithm uses an active set method for demonstrably fast and efficient inference. We experimentally show the benefits of our algorithm on constituency parsing and semantic role labeling. For parsing, unlike unconstrained approaches, our algorithm always generates valid output, incurring only a small drop in performance. For semantic role labeling, imposing constraints using our algorithm corrects common errors, improving F1 by 1.5 points. These benefits increase in low-resource settings. Our active set method achieves a 5.2x relative speed-up over a naive approach.

Combining Discourse Markers and Cross-lingual Embeddings for Synonym–Antonym Classification
Michael Roth | Shyam Upadhyay
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)

It is well-known that distributional semantic approaches have difficulty in distinguishing between synonyms and antonyms (Grefenstette, 1992; Padó and Lapata, 2003). Recent work has shown that supervision available in English for this task (e.g., lexical resources) can be transferred to other languages via cross-lingual word embeddings. However, this kind of transfer misses monolingual distributional information available in a target language, such as contrast relations that are indicative of antonymy (e.g. hot ... while ... cold). In this work, we improve the transfer by exploiting monolingual information, expressed in the form of co-occurrences with discourse markers that convey contrast. Our approach makes use of less than a dozen markers, which can easily be obtained for many languages. Compared to a baseline using only cross-lingual embeddings, we show absolute improvements of 4–10% F1-score in Vietnamese and Hindi.


CogCompNLP: Your Swiss Army Knife for NLP
Daniel Khashabi | Mark Sammons | Ben Zhou | Tom Redman | Christos Christodoulopoulos | Vivek Srikumar | Nicholas Rizzolo | Lev Ratinov | Guanheng Luo | Quang Do | Chen-Tse Tsai | Subhro Roy | Stephen Mayhew | Zhili Feng | John Wieting | Xiaodong Yu | Yangqiu Song | Shashank Gupta | Shyam Upadhyay | Naveen Arivazhagan | Qiang Ning | Shaoshi Ling | Dan Roth
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)

Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences
Daniel Khashabi | Snigdha Chaturvedi | Michael Roth | Shyam Upadhyay | Dan Roth
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 reading comprehension challenge in which questions can only be answered by taking into account information from multiple sentences. We solicit and verify questions and answers for this challenge through a 4-step crowdsourcing experiment. Our challenge dataset contains 6,500+ questions for 1000+ paragraphs across 7 different domains (elementary school science, news, travel guides, fiction stories, etc) bringing in linguistic diversity to the texts and to the questions wordings. On a subset of our dataset, we found human solvers to achieve an F1-score of 88.1%. We analyze a range of baselines, including a recent state-of-art reading comprehension system, and demonstrate the difficulty of this challenge, despite a high human performance. The dataset is the first to study multi-sentence inference at scale, with an open-ended set of question types that requires reasoning skills.

Robust Cross-Lingual Hypernymy Detection Using Dependency Context
Shyam Upadhyay | Yogarshi Vyas | Marine Carpuat | Dan Roth
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Cross-lingual Hypernymy Detection involves determining if a word in one language (“fruit”) is a hypernym of a word in another language (“pomme” i.e. apple in French). The ability to detect hypernymy cross-lingually can aid in solving cross-lingual versions of tasks such as textual entailment and event coreference. We propose BiSparse-Dep, a family of unsupervised approaches for cross-lingual hypernymy detection, which learns sparse, bilingual word embeddings based on dependency contexts. We show that BiSparse-Dep can significantly improve performance on this task, compared to approaches based only on lexical context. Our approach is also robust, showing promise for low-resource settings: our dependency-based embeddings can be learned using a parser trained on related languages, with negligible loss in performance. We also crowd-source a challenging dataset for this task on four languages – Russian, French, Arabic, and Chinese. Our embeddings and datasets are publicly available.

Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages
Shyam Upadhyay | Jordan Kodner | Dan Roth
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Generating the English transliteration of a name written in a foreign script is an important and challenging step in multilingual knowledge acquisition and information extraction. Existing approaches to transliteration generation require a large (>5000) number of training examples. This difficulty contrasts with transliteration discovery, a somewhat easier task that involves picking a plausible transliteration from a given list. In this work, we present a bootstrapping algorithm that uses constrained discovery to improve generation, and can be used with as few as 500 training examples, which we show can be sourced from annotators in a matter of hours. This opens the task to languages for which large number of training examples are unavailable. We evaluate transliteration generation performance itself, as well the improvement it brings to cross-lingual candidate generation for entity linking, a typical downstream task. We present a comprehensive evaluation of our approach on nine languages, each written in a unique script.

Joint Multilingual Supervision for Cross-lingual Entity Linking
Shyam Upadhyay | Nitish Gupta | Dan Roth
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

Cross-lingual Entity Linking (XEL) aims to ground entity mentions written in any language to an English Knowledge Base (KB), such as Wikipedia. XEL for most languages is challenging, owing to limited availability of resources as supervision. We address this challenge by developing the first XEL approach that combines supervision from multiple languages jointly. This enables our approach to: (a) augment the limited supervision in the target language with additional supervision from a high-resource language (like English), and (b) train a single entity linking model for multiple languages, improving upon individually trained models for each language. Extensive evaluation on three benchmark datasets across 8 languages shows that our approach significantly improves over the current state-of-the-art. We also provide analyses in two limited resource settings: (a) zero-shot setting, when no supervision in the target language is available, and in (b) low-resource setting, when some supervision in the target language is available. Our analysis provides insights into the limitations of zero-shot XEL approaches in realistic scenarios, and shows the value of joint supervision in low-resource settings.


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A Consolidated Open Knowledge Representation for Multiple Texts
Rachel Wities | Vered Shwartz | Gabriel Stanovsky | Meni Adler | Ori Shapira | Shyam Upadhyay | Dan Roth | Eugenio Martinez Camara | Iryna Gurevych | Ido Dagan
Proceedings of the 2nd Workshop on Linking Models of Lexical, Sentential and Discourse-level Semantics

We propose to move from Open Information Extraction (OIE) ahead to Open Knowledge Representation (OKR), aiming to represent information conveyed jointly in a set of texts in an open text-based manner. We do so by consolidating OIE extractions using entity and predicate coreference, while modeling information containment between coreferring elements via lexical entailment. We suggest that generating OKR structures can be a useful step in the NLP pipeline, to give semantic applications an easy handle on consolidated information across multiple texts.

Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context
Shyam Upadhyay | Kai-Wei Chang | Matt Taddy | Adam Kalai | James Zou
Proceedings of the 2nd Workshop on Representation Learning for NLP

Word embeddings, which represent a word as a point in a vector space, have become ubiquitous to several NLP tasks. A recent line of work uses bilingual (two languages) corpora to learn a different vector for each sense of a word, by exploiting crosslingual signals to aid sense identification. We present a multi-view Bayesian non-parametric algorithm which improves multi-sense wor d embeddings by (a) using multilingual (i.e., more than two languages) corpora to significantly improve sense embeddings beyond what one achieves with bilingual information, and (b) uses a principled approach to learn a variable number of senses per word, in a data-driven manner. Ours is the first approach with the ability to leverage multilingual corpora efficiently for multi-sense representation learning. Experiments show that multilingual training significantly improves performance over monolingual and bilingual training, by allowing us to combine different parallel corpora to leverage multilingual context. Multilingual training yields comparable performance to a state of the art monolingual model trained on five times more training data.

Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems
Shyam Upadhyay | Ming-Wei Chang
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We propose a new evaluation for automatic solvers for algebra word problems, which can identify mistakes that existing evaluations overlook. Our proposal is to evaluate such solvers using derivations, which reflect how an equation system was constructed from the word problem. To accomplish this, we develop an algorithm for checking the equivalence between two derivations, and show how derivation annotations can be semi-automatically added to existing datasets. To make our experiments more comprehensive, we include the derivation annotation for DRAW-1K, a new dataset containing 1000 general algebra word problems. In our experiments, we found that the annotated derivations enable a more accurate evaluation of automatic solvers than previously used metrics. We release derivation annotations for over 2300 algebra word problems for future evaluations.


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“Making the News”: Identifying Noteworthy Events in News Articles
Shyam Upadhyay | Christos Christodoulopoulos | Dan Roth
Proceedings of the Fourth Workshop on Events

Revisiting the Evaluation for Cross Document Event Coreference
Shyam Upadhyay | Nitish Gupta | Christos Christodoulopoulos | Dan Roth
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Cross document event coreference (CDEC) is an important task that aims at aggregating event-related information across multiple documents. We revisit the evaluation for CDEC, and discover that past works have adopted different, often inconsistent, evaluation settings, which either overlook certain mistakes in coreference decisions, or make assumptions that simplify the coreference task considerably. We suggest a new evaluation methodology which overcomes these limitations, and allows for an accurate assessment of CDEC systems. Our new evaluation setting better reflects the corpus-wide information aggregation ability of CDEC systems by separating event-coreference decisions made across documents from those made within a document. In addition, we suggest a better baseline for the task and semi-automatically identify several inconsistent annotations in the evaluation dataset.

Cross-lingual Models of Word Embeddings: An Empirical Comparison
Shyam Upadhyay | Manaal Faruqui | Chris Dyer | Dan Roth
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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

Equation Parsing : Mapping Sentences to Grounded Equations
Subhro Roy | Shyam Upadhyay | Dan Roth
Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing


The University of Illinois submission to the WMT 2015 Shared Translation Task
Lane Schwartz | Bill Bryce | Chase Geigle | Sean Massung | Yisi Liu | Haoruo Peng | Vignesh Raja | Subhro Roy | Shyam Upadhyay
Proceedings of the Tenth Workshop on Statistical Machine Translation