Keerthiram Murugesan


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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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Efficient Text-based Reinforcement Learning by Jointly Leveraging State and Commonsense Graph Representations
Keerthiram Murugesan | Mattia Atzeni | Pavan Kapanipathi | Kartik Talamadupula | Mrinmaya Sachan | Murray Campbell
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Text-based games (TBGs) have emerged as useful benchmarks for evaluating progress at the intersection of grounded language understanding and reinforcement learning (RL). Recent work has proposed the use of external knowledge to improve the efficiency of RL agents for TBGs. In this paper, we posit that to act efficiently in TBGs, an agent must be able to track the state of the game while retrieving and using relevant commonsense knowledge. Thus, we propose an agent for TBGs that induces a graph representation of the game state and jointly grounds it with a graph of commonsense knowledge from ConceptNet. This combination is achieved through bidirectional knowledge graph attention between the two symbolic representations. We show that agents that incorporate commonsense into the game state graph outperform baseline agents.