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Found 10 papers, 1 papers with code
PODTILE: Facilitating Podcast Episode Browsing with Auto-generated Chapters
To preserve context, each input text is augmented with global context, including the episode's title, description, and previous chapter titles.
SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization
In this work, we revisit the use of NLI for inconsistency detection, finding that past work suffered from a mismatch in input granularity between NLI datasets (sentence-level), and inconsistency detection (document level).
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representations
Dense retrieval (DR) methods conduct text retrieval by first encoding texts in the embedding space and then matching them by nearest neighbor search.
Zero-Shot Dense Retrieval with Momentum Adversarial Domain Invariant Representation
Dense retrieval (DR) methods conduct text retrieval by first encoding texts in the embedding space and then matching them by nearest neighbor search.
Domain-Specific Pretraining for Vertical Search: Case Study on Biomedical Literature
A prominent case in point is the explosion of the biomedical literature on COVID-19, which swelled to hundreds of thousands of papers in a matter of months.
Pretrain Knowledge-Aware Language Models
Rather, we simply signal the existence of entities to the input of the transformer in pretraining, with an entity-extended tokenizer; and at the output, with an additional entity prediction task.
Analyzing and Learning from User Interactions for Search Clarification
We also propose a model for learning representation for clarifying questions based on the user interaction data as implicit feedback.
Generic Intent Representation in Web Search
This paper presents GEneric iNtent Encoder (GEN Encoder) which learns a distributed representation space for user intent in search.
Characterizing and Predicting Enterprise Email Reply Behavior
Email is still among the most popular online activities.
Using Shortlists to Support Decision Making and Improve Recommender System Performance
From a machine learning perspective, adding items to the shortlist generates a new implicit feedback signal as a by-product of exploration and decision making which can improve recommendation quality.
