An Information Retrieval Approach to Short Text Conversation (1408.6988v1)

Abstract: Human computer conversation is regarded as one of the most difficult problems in artificial intelligence. In this paper, we address one of its key sub-problems, referred to as short text conversation, in which given a message from human, the computer returns a reasonable response to the message. We leverage the vast amount of short conversation data available on social media to study the issue. We propose formalizing short text conversation as a search problem at the first step, and employing state-of-the-art information retrieval (IR) techniques to carry out the task. We investigate the significance as well as the limitation of the IR approach. Our experiments demonstrate that the retrieval-based model can make the system behave rather "intelligently", when combined with a huge repository of conversation data from social media.

• The paper presents a retrieval framework that reformulates short text conversation as an IR search problem using a three-stage process.
• It employs translation-based, deep matching, and topic-word models to bridge lexical gaps and capture semantic cues in conversational data.
• Empirical results on a Weibo dataset show enhanced precision, offering practical insights for improving chatbots and dialogue systems.

Overview of Information Retrieval in Short Text Conversation

The paper "An Information Retrieval Approach to Short Text Conversation" explores an innovative method for addressing short text conversations (STC) by framing the task as an Information Retrieval (IR) problem. The authors utilize an extensive corpus of short conversational data from social media platforms to model the interaction between a query and an appropriate response. The primary novelty of the work lies in leveraging state-of-the-art IR techniques enhanced by additional semantic and topic-based features to facilitate these human-computer exchanges.

The primary objective is to retrieve an apt response to a given input query from a large repository of post-comment pairs sourced from social media. This is tackled by representing the task through a retrieval-based framework while carefully combining a suite of sophisticated matching models for improving response accuracy. The research presents an innovative approach involving a blend of retrieval techniques, including both basic linear models and advanced semantic matching models, alongside a translation-based LLM and a proposed topic-word model.

Core Contributions

1. Framework for Retrieval-based STC: The authors introduce a structured framework that formulates STC as a search problem. This is tackled through three stages: retrieval, semantic matching, and ranking, utilizing learning to rank methods.
2. Sophisticated Matching Models:
   • Translation-based LLM (TransLM): This model addresses lexical gaps by translating words between the query and potential responses, enhancing semantic similarity.
   • Deep Matching Model (DeepMatch): Employing a deep neural network architecture, this model captures complex matching relations beyond surface lexical similarities.
   • Topic-Word Model: A novel feature for identifying dominant topics within a query to improve relevance assessment of responses.
3. Empirical Validation and Datasets: The framework’s efficacy is confirmed with a newly crafted and publically available Weibo dataset, offering a valuable resource to dissect language interaction patterns within short text exchanges.

Results

The paper quantitatively demonstrates that integrating TransLM, DeepMatch, and TopicWord models increases the precision of short text conversations. Notably, precision at the top rank improves to 0.637 when uniting all matching features, underscoring the robustness of distinguishing meaningful interactions amidst noisy data.

Implications and Future Directions

From a practical perspective, this work has significant implications for enhancing user interaction technologies such as chatbots, automated customer service systems, and digital personal assistants. Theoretically, it paves a pathway for further exploration into nuanced linguistic and discourse features in conversation modeling.

The script for future research lies in addressing limitations such as entity association, logic consistency, and maintaining coherence over multiple conversation turns. Further investigation into these aspects could provide enhancement to passing intricate dialogue-based intelligence benchmarks akin to the Turing Test.

In conclusion, the research effectively bridges the gap between IR and dialogue systems, providing a rich ground for exploiting social conversational data with advanced retrieval and matching methodologies. As AI and natural language technologies evolve, the frameworks and insights crafted here will remain pivotal in progressing the domain of human-computer interaction.