565 citations · 730 across the 17 of their papers we have counts for
15 papers · 1 filter
Efficient Encoders for Streaming Sequence Tagging
Ayush Kaushal, Aditya Gupta, Shyam Upadhyay +1
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 st…
CST5: Data Augmentation for Code-Switched Semantic Parsing
Anmol Agarwal, Jigar Gupta, Rahul Goel +3
Extending semantic parsers to code-switched input has been a challenging problem, primarily due to a lack of supervised training data. In this work, we introduce CST5, a new data a…
Streaming Intended Query Detection using E2E Modeling for Continued Conversation
Shuo-yiin Chang, Guru Prakash, Zelin Wu +7
In voice-enabled applications, a predetermined hotword isusually used to activate a device in order to attend to the query.However, speaking queries followed by a hotword each time…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
TableFormer: Robust Transformer Modeling for Table-Text Encoding
Jingfeng Yang, Aditya Gupta, Shyam Upadhyay +3
Understanding tables is an important aspect of natural language understanding. Existing models for table understanding require linearization of the table structure, where row or co…
TIMEDIAL: Temporal Commonsense Reasoning in Dialog
Lianhui Qin, Aditya Gupta, Shyam Upadhyay +3
Everyday conversations require understanding everyday events, which in turn, requires understanding temporal commonsense concepts interwoven with those events. Despite recent progr…
Disfl-QA: A Benchmark Dataset for Understanding Disfluencies in Question Answering
Aditya Gupta, Jiacheng Xu, Shyam Upadhyay +2
Disfluencies is an under-studied topic in NLP, even though it is ubiquitous in human conversation. This is largely due to the lack of datasets containing disfluencies. In this pape…
Attention Interpretability Across NLP Tasks
Shikhar Vashishth, Shyam Upadhyay, Gaurav Singh Tomar +1
The attention layer in a neural network model provides insights into the model's reasoning behind its prediction, which are usually criticized for being opaque. Recently, seemingly…
Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages
Shyam Upadhyay, Jordan Kodner, Dan Roth
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.…
Joint Multilingual Supervision for Cross-lingual Entity Linking
Shyam Upadhyay, Nitish Gupta, Dan Roth
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 challengin…
Robust Cross-lingual Hypernymy Detection using Dependency Context
Shyam Upadhyay, Yogarshi Vyas, Marine Carpuat +1
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 t…
Beyond Bilingual: Multi-sense Word Embeddings using Multilingual Context
Shyam Upadhyay, Kai-Wei Chang, Matt Taddy +2
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 l…
Equation Parsing: Mapping Sentences to Grounded Equations
Subhro Roy, Shyam Upadhyay, Dan Roth
Identifying mathematical relations expressed in text is essential to understanding a broad range of natural language text from election reports, to financial news, to sport comment…
Annotating Derivations: A New Evaluation Strategy and Dataset for Algebra Word Problems
Shyam Upadhyay, Ming-Wei Chang
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 solver…
Cross-lingual Models of Word Embeddings: An Empirical Comparison
Shyam Upadhyay, Manaal Faruqui, Chris Dyer +1
Despite interest in using cross-lingual knowledge to learn word embeddings for various tasks, a systematic comparison of the possible approaches is lacking in the literature. We pe…