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Found 11 papers, 9 papers with code
Improving Content Retrievability in Search with Controllable Query Generation
A pre-requisite to discover an entity, e. g. a book, with a search engine is that the entity is retrievable, i. e. there are queries for which the system will surface such entity in the top results.
Do the Findings of Document and Passage Retrieval Generalize to the Retrieval of Responses for Dialogues?
A number of learned sparse and dense retrieval approaches have recently been proposed and proven effective in tasks such as passage retrieval and document retrieval.
Sparse and Dense Approaches for the Full-rank Retrieval of Responses for Dialogues
Ranking responses for a given dialogue context is a popular benchmark in which the setup is to re-rank the ground-truth response over a limited set of $n$ responses, where $n$ is typically 10.
Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators
Our experimental results across two datasets for two IR tasks reveal that retrieval pipelines are not robust to these query variations, with effectiveness drops of $\approx20\%$ on average.
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search
According to the Probability Ranking Principle (PRP), ranking documents in decreasing order of their probability of relevance leads to an optimal document ranking for ad-hoc retrieval.
On the Calibration and Uncertainty of Neural Learning to Rank Models
Our experimental results on the ad-hoc retrieval task of conversation response ranking reveal that (i) BERT-based rankers are not robustly calibrated and that stochastic BERT-based rankers yield better calibration; and (ii) uncertainty estimation is beneficial for both risk-aware neural ranking, i. e. taking into account the uncertainty when ranking documents, and for predicting unanswerable conversational contexts.
Weakly Supervised Label Smoothing
Inspired by our investigation of LS in the context of neural L2R models, we propose a novel technique called Weakly Supervised Label Smoothing (WSLS) that takes advantage of the retrieval scores of the negative sampled documents as a weak supervision signal in the process of modifying the ground-truth labels.
Slice-Aware Neural Ranking
Understanding when and why neural ranking models fail for an IR task via error analysis is an important part of the research cycle.
What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation
Overall, our analyses and experiments show that: (i) BERT has knowledge stored in its parameters about the content of books, movies and music; (ii) it has more content-based knowledge than collaborative-based knowledge; and (iii) fails on conversational recommendation when faced with adversarial data.
Curriculum Learning Strategies for IR: An Empirical Study on Conversation Response Ranking
Curriculum learning has recently been shown to improve neural models' effectiveness by sampling batches non-uniformly, going from easy to difficult instances during training.
Introducing MANtIS: a novel Multi-Domain Information Seeking Dialogues Dataset
Conversational search is an approach to information retrieval (IR), where users engage in a dialogue with an agent in order to satisfy their information needs.
