Search Results for author: Chulaka Gunasekara

Found 21 papers, 8 papers with code

TWEETSUMM - A Dialog Summarization Dataset for Customer Service

no code implementations Findings (EMNLP) 2021 Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov

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.

Extractive Summarization Unsupervised Extractive Summarization

Using Question Answering Rewards to Improve Abstractive Summarization

1 code implementation Findings (EMNLP) 2021 Chulaka Gunasekara, Guy Feigenblat, Benjamin Sznajder, Ranit Aharonov, Sachindra Joshi

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.

Abstractive Text Summarization Question Answering

Evaluating Robustness of Dialogue Summarization Models in the Presence of Naturally Occurring Variations

no code implementations15 Nov 2023 Ankita Gupta, Chulaka Gunasekara, Hui Wan, Jatin Ganhotra, Sachindra Joshi, Marina Danilevsky

We find that both fine-tuned and instruction-tuned models are affected by input variations, with the latter being more susceptible, particularly to dialogue-level perturbations.

The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers

1 code implementation2 May 2023 Ariel Gera, Roni Friedman, Ofir Arviv, Chulaka Gunasekara, Benjamin Sznajder, Noam Slonim, Eyal Shnarch

Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are presumed to be less informative.

Language Modelling Text Generation

Semi-Structured Object Sequence Encoders

no code implementations3 Jan 2023 Rudra Murthy V, Riyaz Bhat, Chulaka Gunasekara, Siva Sankalp Patel, Hui Wan, Tejas Indulal Dhamecha, Danish Contractor, Marina Danilevsky

In this paper we explore the task of modeling semi-structured object sequences; in particular, we focus our attention on the problem of developing a structure-aware input representation for such sequences.

Object

Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization

no code implementations29 Mar 2022 Benjamin Sznajder, Chulaka Gunasekara, Guy Lev, Sachin Joshi, Eyal Shnarch, Noam Slonim

We observe that there are different heuristics that are associated with summaries of different perspectives, and explore these heuristics to create weak-labeled data for intermediate training of the models before fine-tuning with scarce human annotated summaries.

Decision Making

TWEETSUMM -- A Dialog Summarization Dataset for Customer Service

1 code implementation23 Nov 2021 Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov

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.

Extractive Summarization Unsupervised Extractive Summarization

Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks

1 code implementation NAACL 2021 Hanjie Chen, Song Feng, Jatin Ganhotra, Hui Wan, Chulaka Gunasekara, Sachindra Joshi, Yangfeng Ji

Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features.

Natural Language Inference Paraphrase Identification +1

doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset

2 code implementations EMNLP 2020 Song Feng, Hui Wan, Chulaka Gunasekara, Siva Sankalp Patel, Sachindra Joshi, Luis A. Lastras

We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents.

Implicit Discourse Relation Classification: We Need to Talk about Evaluation

no code implementations ACL 2020 Najoung Kim, Song Feng, Chulaka Gunasekara, Luis Lastras

Implicit relation classification on Penn Discourse TreeBank (PDTB) 2. 0 is a common benchmark task for evaluating the understanding of discourse relations.

Classification General Classification +3

Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks

no code implementations5 Nov 2019 Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang, Kshitij Fadnis, Chulaka Gunasekara, Bassem Makni, Nicholas Mattei, Kartik Talamadupula, Achille Fokoue

A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.

Knowledge Graphs Natural Language Inference

Cannot find the paper you are looking for? You can Submit a new open access paper.