64 citations · 98 across the 11 of their papers we have counts for
11 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…
Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?
Xilun Chen, Kushal Lakhotia, Barlas Oğuz +6
Despite their recent popularity and well-known advantages, dense retrievers still lag behind sparse methods such as BM25 in their ability to reliably match salient phrases and rare…
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…
UniK-QA: Unified Representations of Structured and Unstructured Knowledge for Open-Domain Question Answering
Barlas Oguz, Xilun Chen, Vladimir Karpukhin +6
We study open-domain question answering with structured, unstructured and semi-structured knowledge sources, including text, tables, lists and knowledge bases. Departing from prior…
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…
F10-SGD: Fast Training of Elastic-net Linear Models for Text Classification and Named-entity Recognition
Stanislav Peshterliev, Alexander Hsieh, Imre Kiss
Voice-assistants text classification and named-entity recognition (NER) models are trained on millions of example utterances. Because of the large datasets, long training time is o…
Active Learning for New Domains in Natural Language Understanding
Stanislav Peshterliev, John Kearney, Abhyuday Jagannatha +2
We explore active learning (AL) for improving the accuracy of new domains in a natural language understanding (NLU) system. We propose an algorithm called Majority-CRF that uses an…
Statistical Model Compression for Small-Footprint Natural Language Understanding
Grant P. Strimel, Kanthashree Mysore Sathyendra, Stanislav Peshterliev
In this paper we investigate statistical model compression applied to natural language understanding (NLU) models. Small-footprint NLU models are important for enabling offline sys…