13 research outputs found
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
Learning to Re-Rank with Contextualized Stopwords
The use of stopwords has been thoroughly studied in traditional Information Retrieval systems, but remains unexplored in the context of neural models. Neural re-ranking models take the full text of both the query and document into account. Naturally, removing tokens that do not carry relevance information provides us with an opportunity to improve the effectiveness by reducing noise and lower document representation caching-storage requirements. In this work we propose a novel contextualized stopword detection mechanism for neural re-ranking models. This mechanism consists of training a sparse vector in order to filter out document tokens from the ranking decision. This vector is learned end-to-end based on the contextualized document representations, allowing the model to filter terms on a per occurrence basis. This leads to a more explainable model, as it reduces noise. We integrate our component into the state-of-the-art interaction-based TK neural re-ranking model. Our experiments on the MS MARCO passage collection and queries from the TREC 2019 Deep Learning Track show that filtering out traditional stopwords prior to the neural model reduces its effectiveness, while learning to filter out contextualized representations improves it