ConvSDG: Session Data Generation for Conversational Search

• URL: http://arxiv.org/abs/2403.11335v1
• Date: Sun, 17 Mar 2024 20:34:40 GMT
• Title: ConvSDG: Session Data Generation for Conversational Search
• Authors: Fengran Mo, Bole Yi, Kelong Mao, Chen Qu, Kaiyu Huang, Jian-Yun Nie,
• Abstract summary: We propose a framework to explore the feasibility of boosting conversational search by using large language models (LLMs) for session data generation. Within this framework, we design dialogue/session-level and query-level data generation with unsupervised and semi-supervised learning. The generated data are used to fine-tune the conversational dense retriever.
• Score: 29.211860955861244
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Conversational search provides a more convenient interface for users to search by allowing multi-turn interaction with the search engine. However, the effectiveness of the conversational dense retrieval methods is limited by the scarcity of training data required for their fine-tuning. Thus, generating more training conversational sessions with relevant labels could potentially improve search performance. Based on the promising capabilities of large language models (LLMs) on text generation, we propose ConvSDG, a simple yet effective framework to explore the feasibility of boosting conversational search by using LLM for session data generation. Within this framework, we design dialogue/session-level and query-level data generation with unsupervised and semi-supervised learning, according to the availability of relevance judgments. The generated data are used to fine-tune the conversational dense retriever. Extensive experiments on four widely used datasets demonstrate the effectiveness and broad applicability of our ConvSDG framework compared with several strong baselines.

Related papers

• ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval [37.24069808198862]
  Conversational search requires accurate interpretation of user intent from complex multi-turn contexts. This paper presents ChatRetriever, which inherits the strong generalization capability of large language models to robustly represent conversational sessions for dense retrieval.
  arXiv  Detail & Related papers  (2024-04-21T07:03:55Z)
• Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries [48.243879779374836]
  Few-shot dialogue state tracking (DST) with Large Language Models (LLM) relies on an effective and efficient conversation retriever to find similar in-context examples for prompt learning. Previous works use raw dialogue context as search keys and queries, and a retriever is fine-tuned with annotated dialogues to achieve superior performance. We handle the task of conversation retrieval based on text summaries of the conversations. A LLM-based conversation summarizer is adopted for query and key generation, which enables effective maximum inner product search.
  arXiv  Detail & Related papers  (2024-02-20T14:31:17Z)
• CONVERSER: Few-Shot Conversational Dense Retrieval with Synthetic Data Generation [32.10366004426449]
  We propose CONVERSER, a framework for training conversational dense retrievers with at most 6 examples of in-domain dialogues. We utilize the in-context learning capability of large language models to generate conversational queries given a passage in the retrieval corpus. Experimental results on conversational retrieval benchmarks OR-QuAC and TREC CAsT 19 show that the proposed CONVERSER achieves comparable performance to fully-supervised models.
  arXiv  Detail & Related papers  (2023-09-13T06:40:24Z)
• SSP: Self-Supervised Post-training for Conversational Search [63.28684982954115]
  We propose fullmodel (model) which is a new post-training paradigm with three self-supervised tasks to efficiently initialize the conversational search model. To verify the effectiveness of our proposed method, we apply the conversational encoder post-trained by model on the conversational search task using two benchmark datasets: CAsT-19 and CAsT-20.
  arXiv  Detail & Related papers  (2023-07-02T13:36:36Z)
• Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models [115.7508325840751]
  The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs) In this paper, we embark on an investigation into the utilization of ChatGPT for conversational recommendation, revealing the inadequacy of the existing evaluation protocol. We propose an interactive Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user simulators.
  arXiv  Detail & Related papers  (2023-05-22T15:12:43Z)
• CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation [75.60156479374416]
  CGoDial is a new challenging and comprehensive Chinese benchmark for Goal-oriented Dialog evaluation. It contains 96,763 dialog sessions and 574,949 dialog turns totally, covering three datasets with different knowledge sources. To bridge the gap between academic benchmarks and spoken dialog scenarios, we either collect data from real conversations or add spoken features to existing datasets via crowd-sourcing.
  arXiv  Detail & Related papers  (2022-11-21T16:21:41Z)
• Weakly Supervised Data Augmentation Through Prompting for Dialogue Understanding [103.94325597273316]
  We present a novel approach that iterates on augmentation quality by applying weakly-supervised filters. We evaluate our methods on the emotion and act classification tasks in DailyDialog and the intent classification task in Facebook Multilingual Task-Oriented Dialogue. For DailyDialog specifically, using 10% of the ground truth data we outperform the current state-of-the-art model which uses 100% of the data.
  arXiv  Detail & Related papers  (2022-10-25T17:01:30Z)
• Database Search Results Disambiguation for Task-Oriented Dialog Systems [37.36255492341847]
  We propose Database Search Result (DSR) Disambiguation, a novel task that focuses on disambiguating database search results. To study this task, we augment the popular task-oriented dialog datasets (MultiWOZ and SGD) with turns that resolve ambiguities by (a) synthetically generating turns through a pre-defined grammar, and (b) collecting human paraphrases for a subset. We find that training on our augmented dialog data improves the model's ability to deal with ambiguous scenarios, without sacrificing performance on unmodified turns.
  arXiv  Detail & Related papers  (2021-12-15T18:56:18Z)
• Conversations with Search Engines: SERP-based Conversational Response Generation [77.1381159789032]
  We create a suitable dataset, the Search as a Conversation (SaaC) dataset, for the development of pipelines for conversations with search engines. We also develop a state-of-the-art pipeline for conversations with search engines, the Conversations with Search Engines (CaSE) using this dataset. CaSE enhances the state-of-the-art by introducing a supporting token identification module and aprior-aware pointer generator.
  arXiv  Detail & Related papers  (2020-04-29T13:07:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.