Rajarshi Das


2022

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Calibration of Machine Reading Systems at Scale
Shehzaad Dhuliawala | Leonard Adolphs | Rajarshi Das | Mrinmaya Sachan
Findings of the Association for Computational Linguistics: ACL 2022

In typical machine learning systems, an estimate of the probability of the prediction is used to assess the system’s confidence in the prediction.This confidence measure is usually uncalibrated; i.e. the system’s confidence in the prediction does not match the true probability of the predicted output.In this paper, we present an investigation into calibrating open setting machine reading systemssuch as open-domain question answering and claim verification systems.We show that calibrating such complex systems which contain discrete retrieval and deep reading components is challenging and current calibration techniques fail to scale to these settings. We propose simple extensions to existing calibration approaches that allows us to adapt them to these settings.Our experimental results reveal that the approach works well, and can be useful to selectively predict answers when question answering systems are posed with unanswerable or out-of-the-training distribution questions.

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Proceedings of the 1st Workshop on Semiparametric Methods in NLP: Decoupling Logic from Knowledge
Rajarshi Das | Patrick Lewis | Sewon Min | June Thai | Manzil Zaheer
Proceedings of the 1st Workshop on Semiparametric Methods in NLP: Decoupling Logic from Knowledge

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DISAPERE: A Dataset for Discourse Structure in Peer Review Discussions
Neha Kennard | Tim O’Gorman | Rajarshi Das | Akshay Sharma | Chhandak Bagchi | Matthew Clinton | Pranay Kumar Yelugam | Hamed Zamani | Andrew McCallum
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

At the foundation of scientific evaluation is the labor-intensive process of peer review. This critical task requires participants to consume vast amounts of highly technical text. Prior work has annotated different aspects of review argumentation, but discourse relations between reviews and rebuttals have yet to be examined. We present DISAPERE, a labeled dataset of 20k sentences contained in 506 review-rebuttal pairs in English, annotated by experts. DISAPERE synthesizes label sets from prior work and extends them to include fine-grained annotation of the rebuttal sentences, characterizing their context in the review and the authors’ stance towards review arguments. Further, we annotate every review and rebuttal sentence. We show that discourse cues from rebuttals can shed light on the quality and interpretation of reviews. Further, an understanding of the argumentative strategies employed by the reviewers and authors provides useful signal for area chairs and other decision makers.

2021

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Case-based Reasoning for Natural Language Queries over Knowledge Bases
Rajarshi Das | Manzil Zaheer | Dung Thai | Ameya Godbole | Ethan Perez | Jay Yoon Lee | Lizhen Tan | Lazaros Polymenakos | Andrew McCallum
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

It is often challenging to solve a complex problem from scratch, but much easier if we can access other similar problems with their solutions — a paradigm known as case-based reasoning (CBR). We propose a neuro-symbolic CBR approach (CBR-KBQA) for question answering over large knowledge bases. CBR-KBQA consists of a nonparametric memory that stores cases (question and logical forms) and a parametric model that can generate a logical form for a new question by retrieving cases that are relevant to it. On several KBQA datasets that contain complex questions, CBR-KBQA achieves competitive performance. For example, on the CWQ dataset, CBR-KBQA outperforms the current state of the art by 11% on accuracy. Furthermore, we show that CBR-KBQA is capable of using new cases without any further training: by incorporating a few human-labeled examples in the case memory, CBR-KBQA is able to successfully generate logical forms containing unseen KB entities as well as relations.

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Long Document Summarization in a Low Resource Setting using Pretrained Language Models
Ahsaas Bajaj | Pavitra Dangati | Kalpesh Krishna | Pradhiksha Ashok Kumar | Rheeya Uppaal | Bradford Windsor | Eliot Brenner | Dominic Dotterrer | Rajarshi Das | Andrew McCallum
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop

Abstractive summarization is the task of compressing a long document into a coherent short document while retaining salient information. Modern abstractive summarization methods are based on deep neural networks which often require large training datasets. Since collecting summarization datasets is an expensive and time-consuming task, practical industrial settings are usually low-resource. In this paper, we study a challenging low-resource setting of summarizing long legal briefs with an average source document length of 4268 words and only 120 available (document, summary) pairs. To account for data scarcity, we used a modern pre-trained abstractive summarizer BART, which only achieves 17.9 ROUGE-L as it struggles with long documents. We thus attempt to compress these long documents by identifying salient sentences in the source which best ground the summary, using a novel algorithm based on GPT-2 language model perplexity scores, that operates within the low resource regime. On feeding the compressed documents to BART, we observe a 6.0 ROUGE-L improvement. Our method also beats several competitive salience detection baselines. Furthermore, the identified salient sentences tend to agree with independent human labeling by domain experts.

2020

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An Instance Level Approach for Shallow Semantic Parsing in Scientific Procedural Text
Daivik Swarup | Ahsaas Bajaj | Sheshera Mysore | Tim O’Gorman | Rajarshi Das | Andrew McCallum
Findings of the Association for Computational Linguistics: EMNLP 2020

In specific domains, such as procedural scientific text, human labeled data for shallow semantic parsing is especially limited and expensive to create. Fortunately, such specific domains often use rather formulaic writing, such that the different ways of expressing relations in a small number of grammatically similar labeled sentences may provide high coverage of semantic structures in the corpus, through an appropriately rich similarity metric. In light of this opportunity, this paper explores an instance-based approach to the relation prediction sub-task within shallow semantic parsing, in which semantic labels from structurally similar sentences in the training set are copied to test sentences. Candidate similar sentences are retrieved using SciBERT embeddings. For labels where it is possible to copy from a similar sentence we employ an instance level copy network, when this is not possible, a globally shared parametric model is employed. Experiments show our approach outperforms both baseline and prior methods by 0.75 to 3 F1 absolute in the Wet Lab Protocol Corpus and 1 F1 absolute in the Materials Science Procedural Text Corpus.

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Probabilistic Case-based Reasoning for Open-World Knowledge Graph Completion
Rajarshi Das | Ameya Godbole | Nicholas Monath | Manzil Zaheer | Andrew McCallum
Findings of the Association for Computational Linguistics: EMNLP 2020

A case-based reasoning (CBR) system solves a new problem by retrieving ‘cases’ that are similar to the given problem. If such a system can achieve high accuracy, it is appealing owing to its simplicity, interpretability, and scalability. In this paper, we demonstrate that such a system is achievable for reasoning in knowledge-bases (KBs). Our approach predicts attributes for an entity by gathering reasoning paths from similar entities in the KB. Our probabilistic model estimates the likelihood that a path is effective at answering a query about the given entity. The parameters of our model can be efficiently computed using simple path statistics and require no iterative optimization. Our model is non-parametric, growing dynamically as new entities and relations are added to the KB. On several benchmark datasets our approach significantly outperforms other rule learning approaches and performs comparably to state-of-the-art embedding-based approaches. Furthermore, we demonstrate the effectiveness of our model in an “open-world” setting where new entities arrive in an online fashion, significantly outperforming state-of-the-art approaches and nearly matching the best offline method.

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ProtoQA: A Question Answering Dataset for Prototypical Common-Sense Reasoning
Michael Boratko | Xiang Li | Tim O’Gorman | Rajarshi Das | Dan Le | Andrew McCallum
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Given questions regarding some prototypical situation — such as Name something that people usually do before they leave the house for work? — a human can easily answer them via acquired experiences. There can be multiple right answers for such questions, with some more common for a situation than others. This paper introduces a new question answering dataset for training and evaluating common sense reasoning capabilities of artificial intelligence systems in such prototypical situations. The training set is gathered from an existing set of questions played in a long-running international trivia game show – Family Feud. The hidden evaluation set is created by gathering answers for each question from 100 crowd-workers. We also propose a generative evaluation task where a model has to output a ranked list of answers, ideally covering all prototypical answers for a question. After presenting multiple competitive baseline models, we find that human performance still exceeds model scores on all evaluation metrics with a meaningful gap, supporting the challenging nature of the task.

2019

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Chains-of-Reasoning at TextGraphs 2019 Shared Task: Reasoning over Chains of Facts for Explainable Multi-hop Inference
Rajarshi Das | Ameya Godbole | Manzil Zaheer | Shehzaad Dhuliawala | Andrew McCallum
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)

This paper describes our submission to the shared task on “Multi-hop Inference Explanation Regeneration” in TextGraphs workshop at EMNLP 2019 (Jansen and Ustalov, 2019). Our system identifies chains of facts relevant to explain an answer to an elementary science examination question. To counter the problem of ‘spurious chains’ leading to ‘semantic drifts’, we train a ranker that uses contextualized representation of facts to score its relevance for explaining an answer to a question. Our system was ranked first w.r.t the mean average precision (MAP) metric outperforming the second best system by 14.95 points.

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Do Multi-hop Readers Dream of Reasoning Chains?
Haoyu Wang | Mo Yu | Xiaoxiao Guo | Rajarshi Das | Wenhan Xiong | Tian Gao
Proceedings of the 2nd Workshop on Machine Reading for Question Answering

General Question Answering (QA) systems over texts require the multi-hop reasoning capability, i.e. the ability to reason with information collected from multiple passages to derive the answer. In this paper we conduct a systematic analysis to assess such an ability of various existing models proposed for multi-hop QA tasks. Specifically, our analysis investigates that whether providing the full reasoning chain of multiple passages, instead of just one final passage where the answer appears, could improve the performance of the existing QA models. Surprisingly, when using the additional evidence passages, the improvements of all the existing multi-hop reading approaches are rather limited, with the highest error reduction of 5.8% on F1 (corresponding to 1.3% improvement) from the BERT model. To better understand whether the reasoning chains indeed could help find the correct answers, we further develop a co-matching-based method that leads to 13.1% error reduction with passage chains when applied to two of our base readers (including BERT). Our results demonstrate the existence of the potential improvement using explicit multi-hop reasoning and the necessity to develop models with better reasoning abilities.

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Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering
Rajarshi Das | Ameya Godbole | Dilip Kavarthapu | Zhiyu Gong | Abhishek Singhal | Mo Yu | Xiaoxiao Guo | Tian Gao | Hamed Zamani | Manzil Zaheer | Andrew McCallum
Proceedings of the 2nd Workshop on Machine Reading for Question Answering

Multi-hop question answering (QA) requires an information retrieval (IR) system that can find multiple supporting evidence needed to answer the question, making the retrieval process very challenging. This paper introduces an IR technique that uses information of entities present in the initially retrieved evidence to learn to ‘hop’ to other relevant evidence. In a setting, with more than 5 million Wikipedia paragraphs, our approach leads to significant boost in retrieval performance. The retrieved evidence also increased the performance of an existing QA model (without any training) on the benchmark by 10.59 F1.

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Optimal Transport-based Alignment of Learned Character Representations for String Similarity
Derek Tam | Nicholas Monath | Ari Kobren | Aaron Traylor | Rajarshi Das | Andrew McCallum
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

String similarity models are vital for record linkage, entity resolution, and search. In this work, we present STANCE–a learned model for computing the similarity of two strings. Our approach encodes the characters of each string, aligns the encodings using Sinkhorn Iteration (alignment is posed as an instance of optimal transport) and scores the alignment with a convolutional neural network. We evaluate STANCE’s ability to detect whether two strings can refer to the same entity–a task we term alias detection. We construct five new alias detection datasets (and make them publicly available). We show that STANCE (or one of its variants) outperforms both state-of-the-art and classic, parameter-free similarity models on four of the five datasets. We also demonstrate STANCE’s ability to improve downstream tasks by applying it to an instance of cross-document coreference and show that it leads to a 2.8 point improvement in Bˆ3 F1 over the previous state-of-the-art approach.

2018

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A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset
Michael Boratko | Harshit Padigela | Divyendra Mikkilineni | Pritish Yuvraj | Rajarshi Das | Andrew McCallum | Maria Chang | Achille Fokoue-Nkoutche | Pavan Kapanipathi | Nicholas Mattei | Ryan Musa | Kartik Talamadupula | Michael Witbrock
Proceedings of the Workshop on Machine Reading for Question Answering

The recent work of Clark et al. (2018) introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into easy and challenge sets. That paper includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them; however, it does not include clear definitions of these types, nor does it offer information about the quality of the labels. We propose a comprehensive set of definitions of knowledge and reasoning types necessary for answering the questions in the ARC dataset. Using ten annotators and a sophisticated annotation interface, we analyze the distribution of labels across the challenge set and statistics related to them. Additionally, we demonstrate that although naive information retrieval methods return sentences that are irrelevant to answering the query, sufficient supporting text is often present in the (ARC) corpus. Evaluating with human-selected relevant sentences improves the performance of a neural machine comprehension model by 42 points.

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An Interface for Annotating Science Questions
Michael Boratko | Harshit Padigela | Divyendra Mikkilineni | Pritish Yuvraj | Rajarshi Das | Andrew McCallum | Maria Chang | Achille Fokoue | Pavan Kapanipathi | Nicholas Mattei | Ryan Musa | Kartik Talamadupula | Michael Witbrock
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Recent work introduces the AI2 Reasoning Challenge (ARC) and the associated ARC dataset that partitions open domain, complex science questions into an Easy Set and a Challenge Set. That work includes an analysis of 100 questions with respect to the types of knowledge and reasoning required to answer them. However, it does not include clear definitions of these types, nor does it offer information about the quality of the labels or the annotation process used. In this paper, we introduce a novel interface for human annotation of science question-answer pairs with their respective knowledge and reasoning types, in order that the classification of new questions may be improved. We build on the classification schema proposed by prior work on the ARC dataset, and evaluate the effectiveness of our interface with a preliminary study involving 10 participants.

2017

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Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks
Rajarshi Das | Arvind Neelakantan | David Belanger | Andrew McCallum
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Our goal is to combine the rich multi-step inference of symbolic logical reasoning with the generalization capabilities of neural networks. We are particularly interested in complex reasoning about entities and relations in text and large-scale knowledge bases (KBs). Neelakantan et al. (2015) use RNNs to compose the distributed semantics of multi-hop paths in KBs; however for multiple reasons, the approach lacks accuracy and practicality. This paper proposes three significant modeling advances: (1) we learn to jointly reason about relations, entities, and entity-types; (2) we use neural attention modeling to incorporate multiple paths; (3) we learn to share strength in a single RNN that represents logical composition across all relations. On a large-scale Freebase+ClueWeb prediction task, we achieve 25% error reduction, and a 53% error reduction on sparse relations due to shared strength. On chains of reasoning in WordNet we reduce error in mean quantile by 84% versus previous state-of-the-art.

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Question Answering on Knowledge Bases and Text using Universal Schema and Memory Networks
Rajarshi Das | Manzil Zaheer | Siva Reddy | Andrew McCallum
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Existing question answering methods infer answers either from a knowledge base or from raw text. While knowledge base (KB) methods are good at answering compositional questions, their performance is often affected by the incompleteness of the KB. Au contraire, web text contains millions of facts that are absent in the KB, however in an unstructured form. Universal schema can support reasoning on the union of both structured KBs and unstructured text by aligning them in a common embedded space. In this paper we extend universal schema to natural language question answering, employing Memory networks to attend to the large body of facts in the combination of text and KB. Our models can be trained in an end-to-end fashion on question-answer pairs. Evaluation results on Spades fill-in-the-blank question answering dataset show that exploiting universal schema for question answering is better than using either a KB or text alone. This model also outperforms the current state-of-the-art by 8.5 F1 points.

2016

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Incorporating Selectional Preferences in Multi-hop Relation Extraction
Rajarshi Das | Arvind Neelakantan | David Belanger | Andrew McCallum
Proceedings of the 5th Workshop on Automated Knowledge Base Construction

2015

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Gaussian LDA for Topic Models with Word Embeddings
Rajarshi Das | Manzil Zaheer | Chris Dyer
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)