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Found 11 papers, 4 papers with code
MIA 2022 Shared Task Submission: Leveraging Entity Representations, Dense-Sparse Hybrids, and Fusion-in-Decoder for Cross-Lingual Question Answering
The first stage consists of multilingual passage retrieval with a hybrid dense and sparse retrieval strategy.
Leveraging Query Resolution and Reading Comprehension for Conversational Passage Retrieval
This paper describes the participation of UvA. ILPS group at the TREC CAsT 2020 track.
A Comparison of Question Rewriting Methods for Conversational Passage Retrieval
Conversational passage retrieval relies on question rewriting to modify the original question so that it no longer depends on the conversation history.
A Wrong Answer or a Wrong Question? An Intricate Relationship between Question Reformulation and Answer Selection in Conversational Question Answering
The dependency between an adequate question formulation and correct answer selection is a very intriguing but still underexplored area.
Open-Domain Question Answering Goes Conversational via Question Rewriting
We introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs.
Question Rewriting for Conversational Question Answering
Conversational question answering (QA) requires the ability to correctly interpret a question in the context of previous conversation turns.
Least squares binary quantization of neural networks
We conduct experiments on the ImageNet dataset and show a reduced accuracy gap when using the proposed least squares quantization algorithms.
An Exploration of Data Augmentation and Sampling Techniques for Domain-Agnostic Question Answering
To produce a domain-agnostic question answering model for the Machine Reading Question Answering (MRQA) 2019 Shared Task, we investigate the relative benefits of large pre-trained language models, various data sampling strategies, as well as query and context paraphrases generated by back-translation.
Pay-Per-Request Deployment of Neural Network Models Using Serverless Architectures
We demonstrate the serverless deployment of neural networks for model inferencing in NLP applications using Amazon{'}s Lambda service for feedforward evaluation and DynamoDB for storing word embeddings.
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities
We demonstrate a JavaScript implementation of a convolutional neural network that performs feedforward inference completely in the browser.
Exploring the Effectiveness of Convolutional Neural Networks for Answer Selection in End-to-End Question Answering
Most work on natural language question answering today focuses on answer selection: given a candidate list of sentences, determine which contains the answer.
