3 citations · 5 across the 4 of their papers we have counts for
9 papers · 1 filter
Multilingual Transfer Learning for QA Using Translation as Data Augmentation
Mihaela Bornea, Lin Pan, Sara Rosenthal +2
Prior work on multilingual question answering has mostly focused on using large multilingual pre-trained language models (LM) to perform zero-shot language-wise learning: train a Q…
Benchmarking Commercial Intent Detection Services with Practice-Driven Evaluations
Haode Qi, Lin Pan, Atin Sood +4
Intent detection is a key component of modern goal-oriented dialog systems that accomplish a user task by predicting the intent of users' text input. There are three primary challe…
Multilingual BERT Post-Pretraining Alignment
Lin Pan, Chung-Wei Hang, Haode Qi +3
We propose a simple method to align multilingual contextual embeddings as a post-pretraining step for improved zero-shot cross-lingual transferability of the pretrained models. Usi…
The TechQA Dataset
Vittorio Castelli, Rishav Chakravarti, Saswati Dana +18
We introduce TechQA, a domain-adaptation question answering dataset for the technical support domain. The TechQA corpus highlights two real-world issues from the automated customer…
Ensembling Strategies for Answering Natural Questions
Anthony Ferritto, Lin Pan, Rishav Chakravarti +4
Many of the top question answering systems today utilize ensembling to improve their performance on tasks such as the Stanford Question Answering Dataset (SQuAD) and Natural Questi…
Frustratingly Easy Natural Question Answering
Lin Pan, Rishav Chakravarti, Anthony Ferritto +5
Existing literature on Question Answering (QA) mostly focuses on algorithmic novelty, data augmentation, or increasingly large pre-trained language models like XLNet and RoBERTa. A…