Customer-obsessed science
Research areas
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November 20, 20254 min readA new evaluation pipeline called FiSCo uncovers hidden biases and offers an assessment framework that evolves alongside language models.
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October 2, 20253 min read
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September 2, 20253 min read
Featured news
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ACL 20232023Paraphrase generation is a long-standing task in natural language processing (NLP). Supervised paraphrase generation models, which rely on human-annotated paraphrase pairs, are cost-inefficient and hard to scale up. On the other hand, automatically annotated paraphrase pairs (e.g., by machine back-translation), usually suffer from the lack of syntactic diversity — the generated paraphrase sentences are
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ACL Findings 20232023Understanding users’ intentions in e-commerce platforms requires commonsense knowledge. In this paper, we present FolkScope, an intention knowledge graph construction framework to reveal the structure of humans’ minds about purchasing items. As commonsense knowledge is usually ineffable and not expressed explicitly, it is challenging to perform information extraction. Thus, we propose a new approach that
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ACL 20232023Identifying granular and actionable topics from customer questions (CQ) posted on e-commerce websites helps surface the missing information on the product detail page expected by customers before making a purchase. Insights on missing information on product page helps brands and sellers enrich the catalog quality to improve the overall customer experience (CX). In this paper, we propose a weakly supervised
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ACL 20232023We present EPIC (English Perspectivist Irony Corpus), the first annotated corpus for irony analysis based on the principles of data perspectivism. The corpus contains short conversations from social media in five regional varieties of English, and it is annotated by contributors from five countries corresponding to those varieties. We analyse the resource along the perspectives induced by the diversity
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ACL 20232023In real-world systems, an important requirement for model updates is to avoid regressions in user experience caused by flips of previously correct classifications to incorrect ones. Multiple techniques for that have been proposed in the recent literature. In this paper, we apply one such technique, focal distillation, to model updates in a goal-oriented dialog system and assess its usefulness in practice
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