Search Results for author: Swarnadeep Saha

Found 14 papers, 8 papers with code

ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness

1 code implementation21 Apr 2023 Archiki Prasad, Swarnadeep Saha, Xiang Zhou, Mohit Bansal

Multi-step reasoning ability is fundamental to many natural language tasks, yet it is unclear what constitutes a good reasoning chain and how to evaluate them.

Informativeness Natural Language Inference

MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation

no code implementations16 Dec 2022 Swarnadeep Saha, Xinyan Velocity Yu, Mohit Bansal, Ramakanth Pasunuru, Asli Celikyilmaz

We propose MURMUR, a neuro-symbolic modular approach to text generation from semi-structured data with multi-step reasoning.

Data-to-Text Generation

Are Hard Examples also Harder to Explain? A Study with Human and Model-Generated Explanations

1 code implementation14 Nov 2022 Swarnadeep Saha, Peter Hase, Nazneen Rajani, Mohit Bansal

We observe that (1) GPT-3 explanations are as grammatical as human explanations regardless of the hardness of the test samples, (2) for easy examples, GPT-3 generates highly supportive explanations but human explanations are more generalizable, and (3) for hard examples, human explanations are significantly better than GPT-3 explanations both in terms of label-supportiveness and generalizability judgements.

Summarization Programs: Interpretable Abstractive Summarization with Neural Modular Trees

1 code implementation21 Sep 2022 Swarnadeep Saha, Shiyue Zhang, Peter Hase, Mohit Bansal

We demonstrate that SP-Search effectively represents the generative process behind human summaries using modules that are typically faithful to their intended behavior.

Abstractive Text Summarization Sentence Fusion

Explanation Graph Generation via Pre-trained Language Models: An Empirical Study with Contrastive Learning

1 code implementation ACL 2022 Swarnadeep Saha, Prateek Yadav, Mohit Bansal

In this work, we study pre-trained language models that generate explanation graphs in an end-to-end manner and analyze their ability to learn the structural constraints and semantics of such graphs.

Contrastive Learning Graph Generation +1

multiPRover: Generating Multiple Proofs for Improved Interpretability in Rule Reasoning

1 code implementation NAACL 2021 Swarnadeep Saha, Prateek Yadav, Mohit Bansal

In order to jointly learn from all proof graphs and exploit the correlations between multiple proofs for a question, we pose this task as a set generation problem over structured output spaces where each proof is represented as a directed graph.

Multi-Label Classification

ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning

1 code implementation EMNLP 2021 Swarnadeep Saha, Prateek Yadav, Lisa Bauer, Mohit Bansal

Recent commonsense-reasoning tasks are typically discriminative in nature, where a model answers a multiple-choice question for a certain context.

Graph Generation Multiple-choice +1

ConjNLI: Natural Language Inference Over Conjunctive Sentences

1 code implementation EMNLP 2020 Swarnadeep Saha, Yixin Nie, Mohit Bansal

Reasoning about conjuncts in conjunctive sentences is important for a deeper understanding of conjunctions in English and also how their usages and semantics differ from conjunctive and disjunctive boolean logic.

Natural Language Inference

PRover: Proof Generation for Interpretable Reasoning over Rules

2 code implementations EMNLP 2020 Swarnadeep Saha, Sayan Ghosh, Shashank Srivastava, Mohit Bansal

First, PROVER generates proofs with an accuracy of 87%, while retaining or improving performance on the QA task, compared to RuleTakers (up to 6% improvement on zero-shot evaluation).

Pre-Training BERT on Domain Resources for Short Answer Grading

no code implementations IJCNLP 2019 Chul Sung, Tejas Dhamecha, Swarnadeep Saha, Tengfei Ma, Vinay Reddy, Rishi Arora

Pre-trained BERT contextualized representations have achieved state-of-the-art results on multiple downstream NLP tasks by fine-tuning with task-specific data.

Language Modelling

Learning Outcomes and Their Relatedness in a Medical Curriculum

no code implementations WS 2019 Sneha Mondal, Tejas Dhamecha, Shantanu Godbole, Smriti Pathak, Red Mendoza, K Gayathri Wijayarathna, Nabil Zary, Swarnadeep Saha, Malolan Chetlur

A typical medical curriculum is organized in a hierarchy of instructional objectives called Learning Outcomes (LOs); a few thousand LOs span five years of study.

Joint Multi-Domain Learning for Automatic Short Answer Grading

no code implementations25 Feb 2019 Swarnadeep Saha, Tejas I. Dhamecha, Smit Marvaniya, Peter Foltz, Renuka Sindhgatta, Bikram Sengupta

On a large-scale industry dataset and a benchmarking dataset, we show that our model performs significantly better than existing techniques which either learn domain-specific models or adapt a generic similarity scoring model from a large corpus.

Benchmarking Domain Adaptation

Bootstrapping for Numerical Open IE

no code implementations ACL 2017 Swarnadeep Saha, Harinder Pal, {Mausam}

We design and release BONIE, the first open numerical relation extractor, for extracting Open IE tuples where one of the arguments is a number or a quantity-unit phrase.

Implicit Relations Open Information Extraction

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