Shubham Agarwal


2024

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KaPQA: Knowledge-Augmented Product Question-Answering
Swetha Eppalapally | Daksh Dangi | Chaithra Bhat | Ankita Gupta | Ruiyi Zhang | Shubham Agarwal | Karishma Bagga | Seunghyun Yoon | Nedim Lipka | Ryan Rossi | Franck Dernoncourt
Proceedings of the 3rd Workshop on Knowledge Augmented Methods for NLP

Question-answering for domain-specific applications has recently attracted much interest due to the latest advancements in large language models (LLMs). However, accurately assessing the performance of these applications remains a challenge, mainly due to the lack of suitable benchmarks that effectively simulate real-world scenarios. To address this challenge, we introduce two product question-answering (QA) datasets focused on Adobe Acrobat and Photoshop products to help evaluate the performance of existing models on domain-specific product QA tasks. Additionally, we propose a novel knowledge-driven RAG-QA framework to enhance the performance of the models in the product QA task. Our experiments demonstrated that inducing domain knowledge through query reformulation allowed for increased retrieval and generative performance when compared to standard RAG-QA methods. This improvement, however, is slight, and thus illustrates the challenge posed by the datasets introduced.

2021

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Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval)
Anya Belz | Shubham Agarwal | Yvette Graham | Ehud Reiter | Anastasia Shimorina
Proceedings of the Workshop on Human Evaluation of NLP Systems (HumEval)

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A Systematic Review of Reproducibility Research in Natural Language Processing
Anya Belz | Shubham Agarwal | Anastasia Shimorina | Ehud Reiter
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Against the background of what has been termed a reproducibility crisis in science, the NLP field is becoming increasingly interested in, and conscientious about, the reproducibility of its results. The past few years have seen an impressive range of new initiatives, events and active research in the area. However, the field is far from reaching a consensus about how reproducibility should be defined, measured and addressed, with diversity of views currently increasing rather than converging. With this focused contribution, we aim to provide a wide-angle, and as near as possible complete, snapshot of current work on reproducibility in NLP,

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The ReproGen Shared Task on Reproducibility of Human Evaluations in NLG: Overview and Results
Anya Belz | Anastasia Shimorina | Shubham Agarwal | Ehud Reiter
Proceedings of the 14th International Conference on Natural Language Generation

The NLP field has recently seen a substantial increase in work related to reproducibility of results, and more generally in recognition of the importance of having shared definitions and practices relating to evaluation. Much of the work on reproducibility has so far focused on metric scores, with reproducibility of human evaluation results receiving far less attention. As part of a research programme designed to develop theory and practice of reproducibility assessment in NLP, we organised the first shared task on reproducibility of human evaluations, ReproGen 2021. This paper describes the shared task in detail, summarises results from each of the reproduction studies submitted, and provides further comparative analysis of the results. Out of nine initial team registrations, we received submissions from four teams. Meta-analysis of the four reproduction studies revealed varying degrees of reproducibility, and allowed very tentative first conclusions about what types of evaluation tend to have better reproducibility.

2020

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Proceedings of the 1st Workshop on Evaluating NLG Evaluation
Shubham Agarwal | Ondřej Dušek | Sebastian Gehrmann | Dimitra Gkatzia | Ioannis Konstas | Emiel Van Miltenburg | Sashank Santhanam
Proceedings of the 1st Workshop on Evaluating NLG Evaluation

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ReproGen: Proposal for a Shared Task on Reproducibility of Human Evaluations in NLG
Anya Belz | Shubham Agarwal | Anastasia Shimorina | Ehud Reiter
Proceedings of the 13th International Conference on Natural Language Generation

Across NLP, a growing body of work is looking at the issue of reproducibility. However, replicability of human evaluation experiments and reproducibility of their results is currently under-addressed, and this is of particular concern for NLG where human evaluations are the norm. This paper outlines our ideas for a shared task on reproducibility of human evaluations in NLG which aims (i) to shed light on the extent to which past NLG evaluations are replicable and reproducible, and (ii) to draw conclusions regarding how evaluations can be designed and reported to increase replicability and reproducibility. If the task is run over several years, we hope to be able to document an overall increase in levels of replicability and reproducibility over time.

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History for Visual Dialog: Do we really need it?
Shubham Agarwal | Trung Bui | Joon-Young Lee | Ioannis Konstas | Verena Rieser
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Visual Dialogue involves “understanding” the dialogue history (what has been discussed previously) and the current question (what is asked), in addition to grounding information in the image, to accurately generate the correct response. In this paper, we show that co-attention models which explicitly encode dialoh history outperform models that don’t, achieving state-of-the-art performance (72 % NDCG on val set). However, we also expose shortcomings of the crowdsourcing dataset collection procedure, by showing that dialogue history is indeed only required for a small amount of the data, and that the current evaluation metric encourages generic replies. To that end, we propose a challenging subset (VisdialConv) of the VisdialVal set and the benchmark NDCG of 63%.

2018

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A Knowledge-Grounded Multimodal Search-Based Conversational Agent
Shubham Agarwal | Ondřej Dušek | Ioannis Konstas | Verena Rieser
Proceedings of the 2018 EMNLP Workshop SCAI: The 2nd International Workshop on Search-Oriented Conversational AI

Multimodal search-based dialogue is a challenging new task: It extends visually grounded question answering systems into multi-turn conversations with access to an external database. We address this new challenge by learning a neural response generation system from the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017). We introduce a knowledge-grounded multimodal conversational model where an encoded knowledge base (KB) representation is appended to the decoder input. Our model substantially outperforms strong baselines in terms of text-based similarity measures (over 9 BLEU points, 3 of which are solely due to the use of additional information from the KB).

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Improving Context Modelling in Multimodal Dialogue Generation
Shubham Agarwal | Ondřej Dušek | Ioannis Konstas | Verena Rieser
Proceedings of the 11th International Conference on Natural Language Generation

In this work, we investigate the task of textual response generation in a multimodal task-oriented dialogue system. Our work is based on the recently released Multimodal Dialogue (MMD) dataset (Saha et al., 2017) in the fashion domain. We introduce a multimodal extension to the Hierarchical Recurrent Encoder-Decoder (HRED) model and show that this extension outperforms strong baselines in terms of text-based similarity metrics. We also showcase the shortcomings of current vision and language models by performing an error analysis on our system’s output.

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Char2char Generation with Reranking for the E2E NLG Challenge
Shubham Agarwal | Marc Dymetman | Éric Gaussier
Proceedings of the 11th International Conference on Natural Language Generation

This paper describes our submission to the E2E NLG Challenge. Recently, neural seq2seq approaches have become mainstream in NLG, often resorting to pre- (respectively post-) processing delexicalization (relexicalization) steps at the word-level to handle rare words. By contrast, we train a simple character level seq2seq model, which requires no pre/post-processing (delexicalization, tokenization or even lowercasing), with surprisingly good results. For further improvement, we explore two re-ranking approaches for scoring candidates. We also introduce a synthetic dataset creation procedure, which opens up a new way of creating artificial datasets for Natural Language Generation.

2017

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A surprisingly effective out-of-the-box char2char model on the E2E NLG Challenge dataset
Shubham Agarwal | Marc Dymetman
Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue

We train a char2char model on the E2E NLG Challenge data, by exploiting “out-of-the-box” the recently released tfseq2seq framework, using some of the standard options offered by this tool. With minimal effort, and in particular without delexicalization, tokenization or lowercasing, the obtained raw predictions, according to a small scale human evaluation, are excellent on the linguistic side and quite reasonable on the adequacy side, the primary downside being the possible omissions of semantic material. However, in a significant number of cases (more than 70%), a perfect solution can be found in the top-20 predictions, indicating promising directions for solving the remaining issues.