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20172024
most citedExploring the Use of Text Classification in the Legal Domain

105 citations · 220 across the 10 of their papers we have counts for

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19 papers · 1 filter

cs.CL2025

Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching

Xiangci Li, Zhiyu Chen, Jason Ingyu Choi +4

The goal of conversational product search (CPS) is to develop an intelligent, chat-based shopping assistant that can directly interact with customers to understand shopping intents…

cs.CL20245 cited

Question Suggestion for Conversational Shopping Assistants Using Product Metadata

Nikhita Vedula, Oleg Rokhlenko, Shervin Malmasi

Digital assistants have become ubiquitous in e-commerce applications, following the recent advancements in Information Retrieval (IR), Natural Language Processing (NLP) and Generat…

cs.CL20223 cited

Reinforced Question Rewriting for Conversational Question Answering

Zhiyu Chen, Jie Zhao, Anjie Fang +3

Conversational Question Answering (CQA) aims to answer questions contained within dialogues, which are not easily interpretable without context. Developing a model to rewrite conve…

cs.CL201937 cited

SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)

Marcos Zampieri, Shervin Malmasi, Preslav Nakov +3

We present the results and the main findings of SemEval-2019 Task 6 on Identifying and Categorizing Offensive Language in Social Media (OffensEval). The task was based on a new dat…

cs.CL2019

UTFPR at SemEval-2019 Task 5: Hate Speech Identification with Recurrent Neural Networks

Gustavo Henrique Paetzold, Shervin Malmasi, Marcos Zampieri

In this paper we revisit the problem of automatically identifying hate speech in posts from social media. We approach the task using a system based on minimalistic compositional Re…

cs.CL2019

Predicting the Type and Target of Offensive Posts in Social Media

Marcos Zampieri, Shervin Malmasi, Preslav Nakov +3

As offensive content has become pervasive in social media, there has been much research in identifying potentially offensive messages. However, previous work on this topic did not…