Query Resolution for Conversational Search with Limited Supervision

In this work we focus on multi-turn passage retrieval as a crucial component of conversational search. One of the key challenges in multi-turn passage retrieval comes from the fact that the current turn query is often underspecified due to zero anaphora, topic change, or topic return. Context from the conversational history can be used to arrive at a better expression of the current turn query, defined as the task of query resolution. In this paper, we model the query resolution task as a binary term classification problem: for each term appearing in the previous turns of the conversation decide whether to add it to the current turn query or not. We propose QuReTeC (Query Resolution by Term Classification), a neural query resolution model based on bidirectional transformers. We propose a distant supervision method to automatically generate training data by using query-passage relevance labels. Such labels are often readily available in a collection either as human annotations or inferred from user interactions. We show that QuReTeC outperforms state-of-the-art models, and furthermore, that our distant supervision method can be used to substantially reduce the amount of human-curated data required to train QuReTeC. We incorporate QuReTeC in a multi-turn, multi-stage passage retrieval architecture and demonstrate its effectiveness on the TREC CAsT dataset.Comment: SIGIR 2020 full conference pape

Training Curricula for Open Domain Answer Re-Ranking

In precision-oriented tasks like answer ranking, it is more important to rank many relevant answers highly than to retrieve all relevant answers. It follows that a good ranking strategy would be to learn how to identify the easiest correct answers first (i.e., assign a high ranking score to answers that have characteristics that usually indicate relevance, and a low ranking score to those with characteristics that do not), before incorporating more complex logic to handle difficult cases (e.g., semantic matching or reasoning). In this work, we apply this idea to the training of neural answer rankers using curriculum learning. We propose several heuristics to estimate the difficulty of a given training sample. We show that the proposed heuristics can be used to build a training curriculum that down-weights difficult samples early in the training process. As the training process progresses, our approach gradually shifts to weighting all samples equally, regardless of difficulty. We present a comprehensive evaluation of our proposed idea on three answer ranking datasets. Results show that our approach leads to superior performance of two leading neural ranking architectures, namely BERT and ConvKNRM, using both pointwise and pairwise losses. When applied to a BERT-based ranker, our method yields up to a 4% improvement in MRR and a 9% improvement in P@1 (compared to the model trained without a curriculum). This results in models that can achieve comparable performance to more expensive state-of-the-art techniques.Comment: Accepted at SIGIR 2020 (long

Query Exposure Prediction for Groups of Documents in Rankings

The main objective of an Information Retrieval system is to provide a user with the most relevant documents to the user's query. To do this, modern IR systems typically deploy a re-ranking pipeline in which a set of documents is retrieved by a lightweight first-stage retrieval process and then re-ranked by a more effective but expensive model. However, the success of a re-ranking pipeline is heavily dependent on the performance of the first stage retrieval, since new documents are not usually identified during the re-ranking stage. Moreover, this can impact the amount of exposure that a particular group of documents, such as documents from a particular demographic group, can receive in the final ranking. For example, the fair allocation of exposure becomes more challenging or impossible if the first stage retrieval returns too few documents from certain groups, since the number of group documents in the ranking affects the exposure more than the documents' positions. With this in mind, it is beneficial to predict the amount of exposure that a group of documents is likely to receive in the results of the first stage retrieval process, in order to ensure that there are a sufficient number of documents included from each of the groups. In this paper, we introduce the novel task of query exposure prediction (QEP). Specifically, we propose the first approach for predicting the distribution of exposure that groups of documents will receive for a given query. Our new approach, called GEP, uses lexical information from individual groups of documents to estimate the exposure the groups will receive in a ranking. Our experiments on the TREC 2021 and 2022 Fair Ranking Track test collections show that our proposed GEP approach results in exposure predictions that are up to 40 % more accurate than the predictions of adapted existing query performance prediction and resource allocation approaches