4 citations · 11 across the 6 of their papers we have counts for
9 papers
Improving Opinion-based Question Answering Systems Through Label Error Detection and Overwrite
Xiao Yang, Ahmed K. Mohamed, Shashank Jain +6
Label error is a ubiquitous problem in annotated data. Large amounts of label error substantially degrades the quality of deep learning models. Existing methods to tackle the label…
Decoupled Transformer for Scalable Inference in Open-domain Question Answering
Haytham ElFadeel, Stan Peshterliev
Large transformer models, such as BERT, achieve state-of-the-art results in machine reading comprehension (MRC) for open-domain question answering (QA). However, transformers have…
Robustly Optimized and Distilled Training for Natural Language Understanding
Haytham ElFadeel, Stan Peshterliev
In this paper, we explore multi-task learning (MTL) as a second pretraining step to learn enhanced universal language representation for transformer language models. We use the MTL…
Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering
Stan Peshterliev, Barlas Oguz, Debojeet Chatterjee +2
Question answering (QA) is an important use case on voice assistants. A popular approach to QA is extractive reading comprehension (RC) which finds an answer span in a text passage…
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
Sewon Min, Jordan Boyd-Graber, Chris Alberti +50
We review the EfficientQA competition from NeurIPS 2020. The competition focused on open-domain question answering (QA), where systems take natural language questions as input and…
Self-Attention Gazetteer Embeddings for Named-Entity Recognition
Stanislav Peshterliev, Christophe Dupuy, Imre Kiss
Recent attempts to ingest external knowledge into neural models for named-entity recognition (NER) have exhibited mixed results. In this work, we present GazSelfAttn, a novel gazet…