Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding (2024)

Abstract: Conversational dense retrieval has shown to be effective in conversational search. However, a major limitation of conversational dense retrieval is their lack of interpretability, hindering intuitive understanding of model behaviors for targeted improvements. This paper presents CONVINV, a simple yet effective approach to shed light on interpretable conversational dense retrieval models. CONVINV transforms opaque conversational session embeddings into explicitly interpretable text while faithfully maintaining their original retrieval performance as much as possible. Such transformation is achieved by training a recently proposed Vec2Text model based on the ad-hoc query encoder, leveraging the fact that the session and query embeddings share the same space in existing conversational dense retrieval.To further enhance interpretability, we propose to incorporate external interpretable query rewrites into the transformation process. Extensive evaluations on three conversational search benchmarks demonstrate that CONVINV can yield more interpretable text and faithfully preserve original retrieval performance than baselines. Our work connects opaque session embeddings with transparent query rewriting, paving the way toward trustworthy conversational search.

## introduction with the rapid development of language modeling, conversational search has emerged as a novel search paradigm and is garnering more and more attention. different from the traditional ad-hoc search paradigm characterized by keyword-based queries and "ten-blue" links @xcite , conversational search empowers users to interact with the search engine through multi-turn natural language conversations to seek information, which brings a more intuitive and efficient search experience @xcite @xcite . in conversational search, the system input is a multi-turn natural language conversation, which may have many linguistic problems such as omissions, co-references, and ambiguities @xcite , posing great challenges for accurately grasping the user's real information needs. recently, conversational dense retrieval (cdr) @xcite @xcite @xcite @xcite , which directly encodes the whole conversational search session and the passages into a unified embedding space to perform matching, has shown to be a promising method to solve this complex search task. compared to another type of method: conversational query rewriting (cqr) @xcite @xcite , which is a two-step method that first reformulates the search session into a decontextualized query rewrite and subsequently inputs this rewrite into existing ad-hoc search models for search, the end-to-end cdr models can be directly optimized towards better search effectiveness @xcite and is more efficient as it avoids the extra latency caused by the rewriting step. however, a notable drawback of conversational dense retrieval is that it inherently lacks interpretability @xcite . by encoding conversations into dense vector embeddings rather than readable text, it becomes opaque how these cdr models comprehend search intent. the absence of interpretability becomes a severe obstacle for developers to comprehend the reasons behind the search results, hindering effective and targeted enhancements to the bad cases of the models @xcite . moreover, the absence of interpretability poses challenges in identifying and addressing potential biases or errors within the models, which could lead to unfair or misleading search results without the possibility of timely correction. in this paper, we present convinv : a simple and effective approach aiming to shed light on the opacity problem of conversational dense retrieval. convinv demystifies the opaque conversational session embeddings by transforming them into explicitly interpretable text while faithfully maintaining their retrieval performance as much as possible. this transformation allows us to intuitively decipher the characteristics of behaviors of different conversational dense retrieval models. figure 1 provides an overview of convinv. specifically, our approach is based on the recently proposed vec2text @xcite , which is a powerful method that can invert any text embedding into its original text given the corresponding text encoder. however, inverting the session embedding into the original session is meaningless as it brings no interpretability. we adapt vec2text to suit our interpretable inversion of conversational session embedding by taking specific advantage of how the conversational session encoders are trained: the session encoder starts from an ad-hoc query encoder and the passage encoder is frozen during the training. this makes the session and query embeddings finally share the same embedding space for retrieval. therefore, we propose to train a vec2text model based on the ad-hoc query encoder to transform the session embedding so that the transformed text is different from the original session, but also maintains a similar retrieval performance when encoding it with the ad-hoc query encoder. to further enhance the interpretability of the transformed text, we directly incorporate well-interpretable external query rewrites into the vec2text transformation process, effectively guiding it to yield more interpretable text. we conduct extensive evaluations on three conversational search benchmarks. compared to baselines, the proposed convinv can transform conversational session embeddings into more interpretable text as well as faithfully restore the original retrieval performance of the session embeddings. in summary, the contributions of our work are: ## methodology in this work, we present convinv, a new approach designed to demystify conversational session embeddings. our approach focuses on transforming these opaque conversational session embeddings into explicitly interpretable text while maintaining their retrieval performance as much as possible. convinv aims to bridge the gap between the mysterious nature of dense embeddings and the necessity for clear, understandable insights in conversational search intent analysis. ## experimental settings this section presents our basic experimental settings. see appendix a.2 for full details. ## conclusion in this paper, we present a novel approach con-vinv to shed light on the interpretability of conversational dense retrieval. by experimenting with two typical conversational dense retrieval models on three conversational search benchmarks, we demonstrate the effectiveness of our approach in providing interpretable text as well as faithfully restoring the original retrieval performance of ses-sion embeddings. our work not only enhances interpretability in conversational dense retrieval but also lays a groundwork for future research toward trustworthy conversational search.