A brief description of my papers:

SIGIR 2020: Having Your Cake and Eating it Too: Training Neural Retrieval for Language Inference without Losing Lexical Match (Short conference paper), Acceptance rate = 30%

Vikas Yadav, Steven Bethard, Mihai Surdeanu 

Preprint and description coming up soon...

ACL 2020: Unsupervised Alignment-based Iterative Evidence Retrieval for Multi-hop Question Answering (Long conference paper) , Acceptance rate = 25%

Vikas Yadav, Steven Bethard, Mihai Surdeanu 

Preprint and description coming up soon...

EMNLP 2019: Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering, (Long conference paper), Codes, Poster, Acceptance rate = 22%

Vikas Yadav, Steven Bethard, Mihai Surdeanu 

We propose an unsupervised strategy for the selection of justification sentences for multi-hop question answering (QA) that (a) maximizes the relevance of the selected sentences,(b) minimizes the overlap between the selected facts, and (c) maximizes the coverage of both question and answer. This unsupervised sentence selection method can be coupled with any supervised QA approach. We show that the sentences selected by our method improve the performance of a state-of-the-art supervised QA model on two multi-hop QA datasets: AI2's Reasoning Challenge (ARC) and Multi-Sentence Reading Comprehension (MultiRC). We obtain new state-of-the-art performance on both datasets among approaches that do not use external resources for training the QA system: 56.82% F1 on ARC (41.24% on Challenge and 64.49% on Easy) and 26.1% EM0 on MultiRC. Our justification sentences have higher quality than the justifications selected by a strong information retrieval baseline, eg, by 5.4% F1 in MultiRC. We also show that our unsupervised selection of justification sentences is more stable across domains than a state-of-the-art supervised sentence selection method.

NAACL 2019: Alignment over Heterogeneous Embeddings for Question Answering, (Long conference paper), Codes, Oral Presentation, Acceptance rate = 23%

Vikas Yadav, Steven Bethard, Mihai Surdeanu 

We propose a simple, fast, and mostly-unsupervised approach for non-factoid question answering (QA) called Alignment over Heterogeneous Embeddings (AHE). AHE simply aligns each word in the question and candidate answer with the most similar word in the retrieved supporting paragraph, and weighs each alignment score with the inverse document frequency of the corresponding question/answer term. AHE’s similarity function operates over embeddings that model the underlying text at different levels of abstraction: character (FLAIR), word (BERT and GloVe), and sentence (InferSent), where the latter is the only supervised component in the proposed approach. Despite its simplicity and lack of supervision, AHE obtains a new state-of-the-art performance on the “Easy” partition of the AI2 Reasoning Challenge (ARC) dataset (64.6% accuracy), top-two performance on the “Challenge” partition of ARC (34.1%), and top-three performance on the WikiQA dataset (74.08% MRR), outperforming many other complex, supervised approaches. Our error analysis indicates that alignments over character, word, and sentence embeddings capture substantially different semantic information. We exploit this with a simple meta-classifier that learns how much to trust the predictions over each representation, which further improves the performance of unsupervised AHE.

SIGIR 2018: Sanity check: A strong alignment and information retrieval baseline for question answering, (Short conference paper), Codes, Poster, , Acceptance rate = 29%

Vikas Yadav, Rebecca Sharp, Mihai Surdeanu 

While increasingly complex approaches to question answering (QA) have been proposed, the true gain of these systems, particularly with respect to their expensive training requirements, can be inflated when they are not compared to adequate baselines. Here we propose an unsupervised, simple, and fast alignment and information retrieval baseline that incorporates two novel contributions: a one-to-many alignment between query and document terms and negative alignment as a proxy for discriminative information. Our approach not only outperforms all conventional baselines as well as many supervised recurrent neural networks, but also approaches the state of the art for supervised systems on three QA datasets. With only three hyperparameters, we achieve 47% on an 8th grade Science QA dataset, 32.9% on a Yahoo! answers QA dataset and 64% MAP on WikiQA.

COLING 2018: A survey on recent advances in named entity recognition from deep learning models, (Long conference paper), Codes, Poster, Acceptance rate = 35%

Vikas Yadav, Steven Bethard 

Named Entity Recognition (NER) is a key component in NLP systems for question answering, information retrieval, relation extraction, etc. NER systems have been studied and developed widely for decades, but accurate systems using deep neural networks (NN) have only been introduced in the last few years. We present a comprehensive survey of deep neural network architectures for NER, and contrast them with previous approaches to NER based on feature engineering and other supervised or semi-supervised learning algorithms. Our results highlight the improvements achieved by neural networks, and show how incorporating some of the lessons learned from past work on feature-based NER systems can yield further improvements.