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Found 10 papers, 1 papers with code
Retrieving Contextual Information for Long-Form Question Answering using Weak Supervision
Long-form question answering (LFQA) aims at generating in-depth answers to end-user questions, providing relevant information beyond the direct answer.
Strong and Efficient Baselines for Open Domain Conversational Question Answering
Unlike the Open Domain Question Answering (ODQA) setting, the conversational (ODConvQA) domain has received limited attention when it comes to reevaluating baselines for both efficiency and effectiveness.
xPQA: Cross-Lingual Product Question Answering across 12 Languages
To study this practical industrial task, we present xPQA, a large-scale annotated cross-lingual PQA dataset in 12 languages across 9 branches, and report results in (1) candidate ranking, to select the best English candidate containing the information to answer a non-English question; and (2) answer generation, to generate a natural-sounding non-English answer based on the selected English candidate.
Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey
Dense retrieval (DR) approaches based on powerful pre-trained language models (PLMs) achieved significant advances and have become a key component for modern open-domain question-answering systems.
From Rewriting to Remembering: Common Ground for Conversational QA Models
In conversational QA, models have to leverage information in previous turns to answer upcoming questions.
Neural Machine Translation Decoding with Terminology Constraints
Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology constraints remains an open problem.
Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment
We describe a batched beam decoding algorithm for NMT with LMBR n-gram posteriors, showing that LMBR techniques still yield gains on top of the best recently reported results with Transformers.
Neural Machine Translation by Minimising the Bayes-risk with Respect to Syntactic Translation Lattices
This makes our approach much more flexible than $n$-best list or lattice rescoring as the neural decoder is not restricted to the SMT search space.
Speed-Constrained Tuning for Statistical Machine Translation Using Bayesian Optimization
We address the problem of automatically finding the parameters of a statistical machine translation system that maximize BLEU scores while ensuring that decoding speed exceeds a minimum value.
