Lu Ji


2024

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Who Responded to Whom: The Joint Effects of Latent Topics and Discourse in Conversation Structure
Lu Ji | Lei Chen | Jing Li | Zhongyu Wei | Qi Zhang | Xuanjing Huang
Proceedings of the 10th SIGHAN Workshop on Chinese Language Processing (SIGHAN-10)

Vast amount of online conversations are produced on a daily basis, resulting in a pressing need to automatic conversation understanding. As a basis to structure a discussion, we identify the responding relations in the conversation discourse, which link response utterances to their initiations. To figure out who responded to whom, here we explore how the consistency of topic contents and dependency of discourse roles indicate such interactions, whereas most prior work ignore the effects of latent factors underlying word occurrences. We propose a neural model to learn latent topics and discourse in word distributions, and predict pairwise initiation-response links via exploiting topic consistency and discourse dependency. Experimental results on both English and Chinese conversations show that our model significantly outperforms the previous state of the arts.

2021

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Discrete Argument Representation Learning for Interactive Argument Pair Identification
Lu Ji | Zhongyu Wei | Jing Li | Qi Zhang | Xuanjing Huang
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

In this paper, we focus on identifying interactive argument pairs from two posts with opposite stances to a certain topic. Considering opinions are exchanged from different perspectives of the discussing topic, we study the discrete representations for arguments to capture varying aspects in argumentation languages (e.g., the debate focus and the participant behavior). Moreover, we utilize hierarchical structure to model post-wise information incorporating contextual knowledge. Experimental results on the large-scale dataset collected from CMV show that our proposed framework can significantly outperform the competitive baselines. Further analyses reveal why our model yields superior performance and prove the usefulness of our learned representations.

2018

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Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model
Lu Ji | Zhongyu Wei | Xiangkun Hu | Yang Liu | Qi Zhang | Xuanjing Huang
Proceedings of the 27th International Conference on Computational Linguistics

In this paper, we investigate the issue of persuasiveness evaluation for argumentative comments. Most of the existing research explores different text features of reply comments on word level and ignores interactions between participants. In general, viewpoints are usually expressed by multiple arguments and exchanged on argument level. To better model the process of dialogical argumentation, we propose a novel co-attention mechanism based neural network to capture the interactions between participants on argument level. Experimental results on a publicly available dataset show that the proposed model significantly outperforms some state-of-the-art methods for persuasiveness evaluation. Further analysis reveals that attention weights computed in our model are able to extract interactive argument pairs from the original post and the reply.