Chulaka Gunasekara


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X-FACTOR: A Cross-metric Evaluation of Factual Correctness in Abstractive Summarization
Subhajit Chaudhury | Sarathkrishna Swaminathan | Chulaka Gunasekara | Maxwell Crouse | Srinivas Ravishankar | Daiki Kimura | Keerthiram Murugesan | Ramón Fernandez Astudillo | Tahira Naseem | Pavan Kapanipathi | Alexander Gray
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Abstractive summarization models often produce factually inconsistent summaries that are not supported by the original article. Recently, a number of fact-consistent evaluation techniques have been proposed to address this issue; however, a detailed analysis of how these metrics agree with one another has yet to be conducted. In this paper, we present X-FACTOR, a cross-evaluation of three high-performing fact-aware abstractive summarization methods. First, we show that summarization models are often fine-tuned on datasets that contain factually inconsistent summaries and propose a fact-aware filtering mechanism that improves the quality of training data and, consequently, the factuality of these models. Second, we propose a corrector module that can be used to improve the factual consistency of generated summaries. Third, we present a re-ranking technique that samples summary instances from the output distribution of a summarization model and re-ranks the sampled instances based on their factuality. Finally, we provide a detailed cross-metric agreement analysis that shows how tuning a model to output summaries based on a particular factuality metric influences factuality as determined by the other metrics. Our goal in this work is to facilitate research that improves the factuality and faithfulness of abstractive summarization models.


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Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks
Hanjie Chen | Song Feng | Jatin Ganhotra | Hui Wan | Chulaka Gunasekara | Sachindra Joshi | Yangfeng Ji
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Explaining neural network models is important for increasing their trustworthiness in real-world applications. Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features. However, for models with text pairs as inputs (e.g., paraphrase identification), existing methods are not sufficient to capture feature interactions between two texts and their simple extension of computing all word-pair interactions between two texts is computationally inefficient. In this work, we propose the Group Mask (GMASK) method to implicitly detect word correlations by grouping correlated words from the input text pair together and measure their contribution to the corresponding NLP tasks as a whole. The proposed method is evaluated with two different model architectures (decomposable attention model and BERT) across four datasets, including natural language inference and paraphrase identification tasks. Experiments show the effectiveness of GMASK in providing faithful explanations to these models.

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Does Structure Matter? Encoding Documents for Machine Reading Comprehension
Hui Wan | Song Feng | Chulaka Gunasekara | Siva Sankalp Patel | Sachindra Joshi | Luis Lastras
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Machine reading comprehension is a challenging task especially for querying documents with deep and interconnected contexts. Transformer-based methods have shown advanced performances on this task; however, most of them still treat documents as a flat sequence of tokens. This work proposes a new Transformer-based method that reads a document as tree slices. It contains two modules for identifying more relevant text passage and the best answer span respectively, which are not only jointly trained but also jointly consulted at inference time. Our evaluation results show that our proposed method outperforms several competitive baseline approaches on two datasets from varied domains.

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Summary Grounded Conversation Generation
Chulaka Gunasekara | Guy Feigenblat | Benjamin Sznajder | Sachindra Joshi | David Konopnicki
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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TWEETSUMM - A Dialog Summarization Dataset for Customer Service
Guy Feigenblat | Chulaka Gunasekara | Benjamin Sznajder | Sachindra Joshi | David Konopnicki | Ranit Aharonov
Findings of the Association for Computational Linguistics: EMNLP 2021

In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue. The goal of the present article is advancing the automation of this task. We introduce the first large scale, high quality, customer care dialog summarization dataset with close to 6500 human annotated summaries. The data is based on real-world customer support dialogs and includes both extractive and abstractive summaries. We also introduce a new unsupervised, extractive summarization method specific to dialogs.

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Using Question Answering Rewards to Improve Abstractive Summarization
Chulaka Gunasekara | Guy Feigenblat | Benjamin Sznajder | Ranit Aharonov | Sachindra Joshi
Findings of the Association for Computational Linguistics: EMNLP 2021

Neural abstractive summarization models have drastically improved in the recent years. However, the summaries generated by these models generally suffer from issues such as: not capturing the critical facts in source documents, and containing facts that are inconsistent with the source documents. In this work, we present a general framework to train abstractive summarization models to alleviate such issues. We first train a sequence-to-sequence model to summarize documents, and then further train this model in a Reinforcement Learning setting with question-answering based rewards. We evaluate the summaries generated by the this framework using multiple automatic measures and human judgements. The experimental results show that the question-answering rewards can be used as a general framework to improve neural abstractive summarization. Particularly, the results from human evaluations show that the summaries generated by our approach is preferred over 30% of the time over the summaries generated by general abstractive summarization models.


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Implicit Discourse Relation Classification: We Need to Talk about Evaluation
Najoung Kim | Song Feng | Chulaka Gunasekara | Luis Lastras
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Implicit relation classification on Penn Discourse TreeBank (PDTB) 2.0 is a common benchmark task for evaluating the understanding of discourse relations. However, the lack of consistency in preprocessing and evaluation poses challenges to fair comparison of results in the literature. In this work, we highlight these inconsistencies and propose an improved evaluation protocol. Paired with this protocol, we report strong baseline results from pretrained sentence encoders, which set the new state-of-the-art for PDTB 2.0. Furthermore, this work is the first to explore fine-grained relation classification on PDTB 3.0. We expect our work to serve as a point of comparison for future work, and also as an initiative to discuss models of larger context and possible data augmentations for downstream transferability.

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Conversational Document Prediction to Assist Customer Care Agents
Jatin Ganhotra | Haggai Roitman | Doron Cohen | Nathaniel Mills | Chulaka Gunasekara | Yosi Mass | Sachindra Joshi | Luis Lastras | David Konopnicki
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

A frequent pattern in customer care conversations is the agents responding with appropriate webpage URLs that address users’ needs. We study the task of predicting the documents that customer care agents can use to facilitate users’ needs. We also introduce a new public dataset which supports the aforementioned problem. Using this dataset and two others, we investigate state-of-the art deep learning (DL) and information retrieval (IR) models for the task. Additionally, we analyze the practicality of such systems in terms of inference time complexity. Our show that an hybrid IR+DL approach provides the best of both worlds.

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doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset
Song Feng | Hui Wan | Chulaka Gunasekara | Siva Patel | Sachindra Joshi | Luis Lastras
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents. Inspired by how the authors compose documents for guiding end users, we first construct dialogue flows based on the content elements that corresponds to higher-level relations across text sections as well as lower-level relations between discourse units within a section. Then we present these dialogue flows to crowd contributors to create conversational utterances. The dataset includes over 4500 annotated conversations with an average of 14 turns that are grounded in over 450 documents from four domains. Compared to the prior document-grounded dialogue datasets, this dataset covers a variety of dialogue scenes in information-seeking conversations. For evaluating the versatility of the dataset, we introduce multiple dialogue modeling tasks and present baseline approaches.

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Agent Assist through Conversation Analysis
Kshitij Fadnis | Nathaniel Mills | Jatin Ganhotra | Haggai Roitman | Gaurav Pandey | Doron Cohen | Yosi Mass | Shai Erera | Chulaka Gunasekara | Danish Contractor | Siva Patel | Q. Vera Liao | Sachindra Joshi | Luis Lastras | David Konopnicki
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Customer support agents play a crucial role as an interface between an organization and its end-users. We propose CAIRAA: Conversational Approach to Information Retrieval for Agent Assistance, to reduce the cognitive workload of support agents who engage with users through conversation systems. CAIRAA monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. We combine traditional information retrieval (IR) approaches with more recent Deep Learning (DL) models to ensure high accuracy and efficient run-time performance in the deployed system. Here, we describe the CAIRAA system and demonstrate its effectiveness in a pilot study via a short video.


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A Large-Scale Corpus for Conversation Disentanglement
Jonathan K. Kummerfeld | Sai R. Gouravajhala | Joseph J. Peper | Vignesh Athreya | Chulaka Gunasekara | Jatin Ganhotra | Siva Sankalp Patel | Lazaros C Polymenakos | Walter Lasecki
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Disentangling conversations mixed together in a single stream of messages is a difficult task, made harder by the lack of large manually annotated datasets. We created a new dataset of 77,563 messages manually annotated with reply-structure graphs that both disentangle conversations and define internal conversation structure. Our data is 16 times larger than all previously released datasets combined, the first to include adjudication of annotation disagreements, and the first to include context. We use our data to re-examine prior work, in particular, finding that 89% of conversations in a widely used dialogue corpus are either missing messages or contain extra messages. Our manually-annotated data presents an opportunity to develop robust data-driven methods for conversation disentanglement, which will help advance dialogue research.

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DSTC7 Task 1: Noetic End-to-End Response Selection
Chulaka Gunasekara | Jonathan K. Kummerfeld | Lazaros Polymenakos | Walter Lasecki
Proceedings of the First Workshop on NLP for Conversational AI

Goal-oriented dialogue in complex domains is an extremely challenging problem and there are relatively few datasets. This task provided two new resources that presented different challenges: one was focused but small, while the other was large but diverse. We also considered several new variations on the next utterance selection problem: (1) increasing the number of candidates, (2) including paraphrases, and (3) not including a correct option in the candidate set. Twenty teams participated, developing a range of neural network models, including some that successfully incorporated external data to boost performance. Both datasets have been publicly released, enabling future work to build on these results, working towards robust goal-oriented dialogue systems.