Md Arafat Sultan

Also published as: Md. Arafat Sultan, Md. Sultan


2023

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Moving Beyond Downstream Task Accuracy for Information Retrieval Benchmarking
Keshav Santhanam | Jon Saad-Falcon | Martin Franz | Omar Khattab | Avi Sil | Radu Florian | Md Arafat Sultan | Salim Roukos | Matei Zaharia | Christopher Potts
Findings of the Association for Computational Linguistics: ACL 2023

Neural information retrieval (IR) systems have progressed rapidly in recent years, in large part due to the release of publicly available benchmarking tasks. Unfortunately, some dimensions of this progress are illusory: the majority of the popular IR benchmarks today focus exclusively on downstream task accuracy and thus conceal the costs incurred by systems that trade away efficiency for quality. Latency, hardware cost, and other efficiency considerations are paramount to the deployment of IR systems in user-facing settings. We propose that IR benchmarks structure their evaluation methodology to include not only metrics of accuracy, but also efficiency considerations such as a query latency and the corresponding cost budget for a reproducible hardware setting. For the popular IR benchmarks MS MARCO and XOR-TyDi, we show how the best choice of IR system varies according to how these efficiency considerations are chosen and weighed. We hope that future benchmarks will adopt these guidelines toward more holistic IR evaluation.

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PrimeQA: The Prime Repository for State-of-the-Art Multilingual Question Answering Research and Development
Avi Sil | Jaydeep Sen | Bhavani Iyer | Martin Franz | Kshitij Fadnis | Mihaela Bornea | Sara Rosenthal | Scott McCarley | Rong Zhang | Vishwajeet Kumar | Yulong Li | Md Arafat Sultan | Riyaz Bhat | Juergen Bross | Radu Florian | Salim Roukos
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers. In this paper, we introduce PrimeQA: a one-stop and open-source QA repository with an aim to democratize QA research and facilitate easy replication of state-of-the-art (SOTA) QA methods. PrimeQA supports core QA functionalities like retrieval and reading comprehension as well as auxiliary capabilities such as question generation. It has been designed as an end-to-end toolkit for various use cases: building front-end applications, replicating SOTA methods on public benchmarks, and expanding pre-existing methods. PrimeQA is available at: https://github.com/primeqa.

2022

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Towards Robust Neural Retrieval with Source Domain Synthetic Pre-Finetuning
Revanth Gangi Reddy | Vikas Yadav | Md Arafat Sultan | Martin Franz | Vittorio Castelli | Heng Ji | Avirup Sil
Proceedings of the 29th International Conference on Computational Linguistics

Research on neural IR has so far been focused primarily on standard supervised learning settings, where it outperforms traditional term matching baselines. Many practical use cases of such models, however, may involve previously unseen target domains. In this paper, we propose to improve the out-of-domain generalization of Dense Passage Retrieval (DPR) - a popular choice for neural IR - through synthetic data augmentation only in the source domain. We empirically show that pre-finetuning DPR with additional synthetic data in its source domain (Wikipedia), which we generate using a fine-tuned sequence-to-sequence generator, can be a low-cost yet effective first step towards its generalization. Across five different test sets, our augmented model shows more robust performance than DPR in both in-domain and zero-shot out-of-domain evaluation.

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Learning Cross-Lingual IR from an English Retriever
Yulong Li | Martin Franz | Md Arafat Sultan | Bhavani Iyer | Young-Suk Lee | Avirup Sil
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We present DR.DECR (Dense Retrieval with Distillation-Enhanced Cross-Lingual Representation), a new cross-lingual information retrieval (CLIR) system trained using multi-stage knowledge distillation (KD). The teacher of DR.DECR relies on a highly effective but computationally expensive two-stage inference process consisting of query translation and monolingual IR, while the student, DR.DECR, executes a single CLIR step. We teach DR.DECR powerful multilingual representations as well as CLIR by optimizing two corresponding KD objectives. Learning useful representations of non-English text from an English-only retriever is accomplished through a cross-lingual token alignment algorithm that relies on the representation capabilities of the underlying multilingual encoders. In both in-domain and zero-shot out-of-domain evaluation, DR.DECR demonstrates far superior accuracy over direct fine-tuning with labeled CLIR data. It is also the best single-model retriever on the XOR-TyDi benchmark at the time of this writing.

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Not to Overfit or Underfit the Source Domains? An Empirical Study of Domain Generalization in Question Answering
Md Arafat Sultan | Avi Sil | Radu Florian
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Machine learning models are prone to overfitting their training (source) domains, which is commonly believed to be the reason why they falter in novel target domains. Here we examine the contrasting view that multi-source domain generalization (DG) is first and foremost a problem of mitigating source domain underfitting: models not adequately learning the signal already present in their multi-domain training data. Experiments on a reading comprehension DG benchmark show that as a model learns its source domains better—using familiar methods such as knowledge distillation (KD) from a bigger model—its zero-shot out-of-domain utility improves at an even faster pace. Improved source domain learning also demonstrates superior out-of-domain generalization over three popular existing DG approaches that aim to limit overfitting. Our implementation of KD-based domain generalization is available via PrimeQA at: https://ibm.biz/domain-generalization-with-kd.

2020

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Answer Span Correction in Machine Reading Comprehension
Revanth Gangi Reddy | Md Arafat Sultan | Efsun Sarioglu Kayi | Rong Zhang | Vittorio Castelli | Avi Sil
Findings of the Association for Computational Linguistics: EMNLP 2020

Answer validation in machine reading comprehension (MRC) consists of verifying an extracted answer against an input context and question pair. Previous work has looked at re-assessing the “answerability” of the question given the extracted answer. Here we address a different problem: the tendency of existing MRC systems to produce partially correct answers when presented with answerable questions. We explore the nature of such errors and propose a post-processing correction method that yields statistically significant performance improvements over state-of-the-art MRC systems in both monolingual and multilingual evaluation.

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GPT-too: A Language-Model-First Approach for AMR-to-Text Generation
Manuel Mager | Ramón Fernandez Astudillo | Tahira Naseem | Md Arafat Sultan | Young-Suk Lee | Radu Florian | Salim Roukos
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Abstract Meaning Representations (AMRs) are broad-coverage sentence-level semantic graphs. Existing approaches to generating text from AMR have focused on training sequence-to-sequence or graph-to-sequence models on AMR annotated data only. In this paper, we propose an alternative approach that combines a strong pre-trained language model with cycle consistency-based re-scoring. Despite the simplicity of the approach, our experimental results show these models outperform all previous techniques on the English LDC2017T10 dataset, including the recent use of transformer architectures. In addition to the standard evaluation metrics, we provide human evaluation experiments that further substantiate the strength of our approach.

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On the Importance of Diversity in Question Generation for QA
Md Arafat Sultan | Shubham Chandel | Ramón Fernandez Astudillo | Vittorio Castelli
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Automatic question generation (QG) has shown promise as a source of synthetic training data for question answering (QA). In this paper we ask: Is textual diversity in QG beneficial for downstream QA? Using top-p nucleus sampling to derive samples from a transformer-based question generator, we show that diversity-promoting QG indeed provides better QA training than likelihood maximization approaches such as beam search. We also show that standard QG evaluation metrics such as BLEU, ROUGE and METEOR are inversely correlated with diversity, and propose a diversity-aware intrinsic measure of overall QG quality that correlates well with extrinsic evaluation on QA.

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Multi-Stage Pre-training for Low-Resource Domain Adaptation
Rong Zhang | Revanth Gangi Reddy | Md Arafat Sultan | Vittorio Castelli | Anthony Ferritto | Radu Florian | Efsun Sarioglu Kayi | Salim Roukos | Avi Sil | Todd Ward
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Transfer learning techniques are particularly useful for NLP tasks where a sizable amount of high-quality annotated data is difficult to obtain. Current approaches directly adapt a pretrained language model (LM) on in-domain text before fine-tuning to downstream tasks. We show that extending the vocabulary of the LM with domain-specific terms leads to further gains. To a bigger effect, we utilize structure in the unlabeled data to create auxiliary synthetic tasks, which helps the LM transfer to downstream tasks. We apply these approaches incrementally on a pretrained Roberta-large LM and show considerable performance gain on three tasks in the IT domain: Extractive Reading Comprehension, Document Ranking and Duplicate Question Detection.

2019

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Cross-Task Knowledge Transfer for Query-Based Text Summarization
Elozino Egonmwan | Vittorio Castelli | Md Arafat Sultan
Proceedings of the 2nd Workshop on Machine Reading for Question Answering

We demonstrate the viability of knowledge transfer between two related tasks: machine reading comprehension (MRC) and query-based text summarization. Using an MRC model trained on the SQuAD1.1 dataset as a core system component, we first build an extractive query-based summarizer. For better precision, this summarizer also compresses the output of the MRC model using a novel sentence compression technique. We further leverage pre-trained machine translation systems to abstract our extracted summaries. Our models achieve state-of-the-art results on the publicly available CNN/Daily Mail and Debatepedia datasets, and can serve as simple yet powerful baselines for future systems. We also hope that these results will encourage research on transfer learning from large MRC corpora to query-based summarization.

2016

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Bayesian Supervised Domain Adaptation for Short Text Similarity
Md Arafat Sultan | Jordan Boyd-Graber | Tamara Sumner
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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Fast and Easy Short Answer Grading with High Accuracy
Md Arafat Sultan | Cristobal Salazar | Tamara Sumner
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

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A Joint Model for Answer Sentence Ranking and Answer Extraction
Md Arafat Sultan | Vittorio Castelli | Radu Florian
Transactions of the Association for Computational Linguistics, Volume 4

Answer sentence ranking and answer extraction are two key challenges in question answering that have traditionally been treated in isolation, i.e., as independent tasks. In this article, we (1) explain how both tasks are related at their core by a common quantity, and (2) propose a simple and intuitive joint probabilistic model that addresses both via joint computation but task-specific application of that quantity. In our experiments with two TREC datasets, our joint model substantially outperforms state-of-the-art systems in both tasks.

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DLS@CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity
Md Arafat Sultan | Steven Bethard | Tamara Sumner
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

2015

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DLS@CU: Sentence Similarity from Word Alignment and Semantic Vector Composition
Md Arafat Sultan | Steven Bethard | Tamara Sumner
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)

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Feature-Rich Two-Stage Logistic Regression for Monolingual Alignment
Md Arafat Sultan | Steven Bethard | Tamara Sumner
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

2014

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Back to Basics for Monolingual Alignment: Exploiting Word Similarity and Contextual Evidence
Md Arafat Sultan | Steven Bethard | Tamara Sumner
Transactions of the Association for Computational Linguistics, Volume 2

We present a simple, easy-to-replicate monolingual aligner that demonstrates state-of-the-art performance while relying on almost no supervision and a very small number of external resources. Based on the hypothesis that words with similar meanings represent potential pairs for alignment if located in similar contexts, we propose a system that operates by finding such pairs. In two intrinsic evaluations on alignment test data, our system achieves F1 scores of 88–92%, demonstrating 1–3% absolute improvement over the previous best system. Moreover, in two extrinsic evaluations our aligner outperforms existing aligners, and even a naive application of the aligner approaches state-of-the-art performance in each extrinsic task.

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DLS@CU: Sentence Similarity from Word Alignment
Md Arafat Sultan | Steven Bethard | Tamara Sumner
Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014)

2013

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DLS@CU-CORE: A Simple Machine Learning Model of Semantic Textual Similarity
Md. Sultan | Steven Bethard | Tamara Sumner
Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 1: Proceedings of the Main Conference and the Shared Task: Semantic Textual Similarity

2012

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Identifying science concepts and student misconceptions in an interactive essay writing tutor
Steven Bethard | Ifeyinwa Okoye | Md. Arafat Sultan | Haojie Hang | James H. Martin | Tamara Sumner
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP