2015 TowardsImprovingDialogueTopicTr
- (Kim et al., 2015) ⇒ Seokhwan Kim, Rafael E Banchs, and Haizhou Li. (2015). “Towards Improving Dialogue Topic Tracking Performances with Wikification of Concept Mentions.” In: 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue.
Subject Headings: Dialogue Topic Tracking.
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Abstract
Dialogue topic tracking aims at analyzing and maintaining topic transitions in on-going dialogues. This paper proposes to utilize Wikification-based features for providing mention-level correspondences to Wikipedia concepts for dialogue topic tracking. The experimental results show that our proposed features can significantly improve the performances of the task in mixed-initiative human-human dialogues.
1. Introduction
Dialogue topic tracking aims at detecting topic transitions and predicting topic categories in ongoing dialogues which address more than a single topic. Since human communications in real-world situations tend to consist of a series of multiple topics even for a single domain, tracking dialogue topics plays a key role in analyzing human-human dialogues as well as improving the naturalness of human-machine interactions by conducting multitopic conversations.
Some researchers (Nakata et al., 2002; Lagus and Kuusisto, 2002; Adams and Martell, 2008) attempted to solve this problem with text categorization approaches for the utterances in a given turn. However, these approaches can only be effective for the cases when users mention the topic-related expressions explicitly in their utterances, because the models for text categorization assume that the proper category for each textual unit can be assigned based only on its own contents. The other direction of dialogue topic tracking made use of external knowledge sources including domain models (Roy and Subramaniam, 2006), heuristics (Young et al., 2007), and agendas (Bohus and Rudnicky, 2003; Lee et al., 2008). While these knowledge-based methods have an advantage of dealing with system-initiative dialogues by controlling dialogue flows based on given resources, they have drawbacks in low flexibility to handle the user’s responses and high costs for building the resources.
Recently, we have proposed to explore domain knowledge fromWikipedia for mixed-initiative dialogue topic tracking without significant costs for building resources (Kim et al., 2014a; Kim et al., 2014b). In these methods, a set of articles that have similar contents to a given dialogue segment are selected using vector space model. Then various types of information obtained from the articles are utilized to learn topic trackers based on kernel methods.
In this work, we focus on the following limitations of our former work in retrieving relevant concepts at a given turn with the term vector similarity between each pair of dialogue segment and Wikipedia article. Firstly, the contents of conversation could be expressed in totally different ways from the descriptions in the actual relevant articles inWikipedia. This mismatch between spoken dialogues and written encyclopedia could bring about inaccuracy in selecting proper Wikipedia articles as sources for domain knowledge. Secondly, a set of articles that are selected by comparing with a whole dialogue segment can be limited to reflect the multiple relevances if more than one concept are actually mentioned in the segment. Lastly, lack of semantic or discourse aspects in concept retrieval could cause a limited capability of the tracker to deal with implicitly mentioned subjects. To solve these issues, we propose to incorporate Wikification (Mihalcea and Csomai, 2007) features for building dialogue topic trackers. The goal of Wikification is resolving ambiguities and variabilities of every mention in natural language by linking the expression to its relevant Wikipedia concept. Since this task is performed using not …
References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2015 TowardsImprovingDialogueTopicTr | Seokhwan Kim Rafael E Banchs Haizhou Li | Towards Improving Dialogue Topic Tracking Performances with Wikification of Concept Mentions |