History-Aware Conversational Dense Retrieval (2024)

Abstract: Conversational search facilitates complex information retrieval by enabling multi-turn interactions between users and the system. Supporting such interactions requires a comprehensive understanding of the conversational inputs to formulate a good search query based on historical information. In particular, the search query should include the relevant information from the previous conversation turns.However, current approaches for conversational dense retrieval primarily rely on fine-tuning a pre-trained ad-hoc retriever using the whole conversational search session, which can be lengthy and noisy. Moreover, existing approaches are limited by the amount of manual supervision signals in the existing datasets.To address the aforementioned issues, we propose a **H**istory-**A**ware **Conv**ersational **D**ense **R**etrieval (HAConvDR) system, which incorporates two ideas: context-denoised query reformulation and automatic mining of supervision signals based on the actual impact of historical turns.Experiments on two public conversational search datasets demonstrate the improved history modeling capability of HAConvDR, in particular for long conversations with topic shifts.

## introduction conversational search is expected to be the next generation of search engines @xcite . it aims to satisfy complex user information needs via multi-turn interactions between a user and the system. in single-turn ad-hoc search, users typically employ stand-alone queries to convey their information requirements @xcite in a brief and clearly-expressed manner. in conversational search, however, queries are usually context-dependent, which highlights the necessity of understanding the search intent within the conversational context. to uncover the user's information need, conversational query rewriting (cqr) @xcite @xcite employs humanrewritten queries to train a rewriting model that generates de-contextualized queries. however, obtaining large-scale manual annotations for this purpose is challenging in practice. besides, cqr models cannot be directly optimized for the downstream retrieval task @xcite . in comparison, a more desirable approach is to perform end-to-end conversational dense retrieval (cdr) by training a query encoder that incorporates conversation history @xcite . since human annotations are usually not available to indicate which previous conversation turns are relevant to the current query, a common practice is to utilize all historical turns to reformulate the current query as the input to the model. however, the conversation history can be lengthy and often includes a substantial amount of noise, i.e., historical turns that are irrelevant to the current query. despite the observation @xcite that conversational sessions often center around a specific topic (e.g., sports), it is worth noting that different turns may focus on different aspects (e.g., match results, or player statistics). some of them are relevant to the current turn, while others may not. this is especially the case when conversations are long. this problem can give rise to the issue of shortcut history dependency @xcite , i.e. the reformulated query depends excessively on the historical turns while neglecting the current query. we illustrate this issue by an example in figure 1 . given the current query q 4 , instead of retrieving the passage p * 4 (addressing the current information need) in top-ranked positions, the retriever ranks p * 3 (addressing historical information needs) higher than p * 4 . to tackle the aforementioned challenge, we put forward haconvdr, a new history-aware conversational dense retrieval method, aiming to leverage the useful information from the history as much as possible to reformulate the current query. our approach consists of two prongs of enhancements as detailed in the following sections. the first prong is to incorporate an explicit denoising mechanism into the model training process so that the model is less affected by the noisy history while being history-aware. to achieve a similar purpose, recent studies @xcite @xcite typically assess whether a historical turn is relevant to the current turn based on the historical query. however, these approaches are inherently lacking because historical queries alone are often not sufficient to fully cover the historical context. to address this shortcoming, we additionally leverage the passages associated with historical queries to better evaluate the intent of a historical turn. specifically, we use a pseudo-labeling approach to assess the relevance and usefulness of the historical turns -whether they contribute to improving the retrieval effectiveness of the current query. we then retain the relevant historical turns for context-denoised query reformulation. the second prong is to mine additional supervision signals to further alleviate the pitfall of shortcut history dependency. despite having contextdenoised queries, a single ground-truth passage (given by the dataset) is often indirect and insufficient to guide the training of conversational retrieval due to the remaining noise in the formulated query. thus, mining additional supervisions, either positive @xcite or negative @xcite , can enhance the original supervision signal and reduce the negative impact by the distractors in the conversation history. different from the aforementioned work that acquires additional supervisions by human annotation or retrieval, we mine pseudo positive and hard negative supervisions from the conversation history based on the same relevance judgment of historical turns used for query reformulation. intuitively, among the topranked historical ground-truth passages in figure 1 , some of them can be highly relevant to the current query, which resembles the pseudo relevant documents in pseudo relevance feedback (prf) @xcite , while others are less relevant and can serve as hard negatives for training. these additional supervisions enable the model to be aware of the usefulness or harmfulness of historical groundtruth passages and leverage them in a history-aware contrastive learning process. we carry out extensive experiments on two conversational search datasets to test the effectiveness of haconvdr. the results show that our method outperforms most existing strong baselines, demonstrating how relevance judgments of historical turns can benefit conversational retrieval. our contributions are summarized as follows: (1) we propose haconvdr to train a history-aware conversational dense retriever by using the groundtruth passage from historical turns as additional supervision signals. (2) we conduct pseudo relevance judgment on selecting historical turns to denoise the context for query reformulation, whose results are the foundation of mining additional supervision signals. (3) we demonstrate the effectiveness of haconvdr by outperforming different types of strong baselines on two public datasets. a series of analyses are conducted to understand how historical ground-truth passages work well to solve the conversation with lots of topic shifts. ## conclusion in this paper, we present a new history-aware contrastive learning strategy for conversational dense retriever training, haconvdr, which is based on context-denoised query reformulation and additional supervision signals mining from historical turns. extensive experimental results on public datasets demonstrate the effectiveness of our model. furthermore, we conduct comprehensive analyses to gain insights into the impact of each component of haconvdr on enhancing search performance and provide valuable insights on how they can work well for conversations with topic shifts.