22 citations · 50 across the 10 of their papers we have counts for
12 papers
Encode Once, Decode Never: Reusing Audio LM Internals for Efficient Temporal Localization
Joseph An, Phillip Keung, Jiaqi Wang +2
Audio language models process input audio into rich frame-level representations, but the standard approach to temporal localization generates timestamps as sequences of text tokens…
Summarization-Based Document IDs for Generative Retrieval with Language Models
Haoxin Li, Daniel Cheng, Phillip Keung +2
Generative retrieval (Wang et al., 2022; Tay et al., 2022) is a popular approach for end-to-end document retrieval that directly generates document identifiers given an input query…
NarrowBERT: Accelerating Masked Language Model Pretraining and Inference
Haoxin Li, Phillip Keung, Daniel Cheng +2
Large-scale language model pretraining is a very successful form of self-supervised learning in natural language processing, but it is increasingly expensive to perform as the mode…
Domain Mismatch Doesn't Always Prevent Cross-Lingual Transfer Learning
Daniel Edmiston, Phillip Keung, Noah A. Smith
Cross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering,…
Unsupervised Bitext Mining and Translation via Self-trained Contextual Embeddings
Phillip Keung, Julian Salazar, Yichao Lu +1
We describe an unsupervised method to create pseudo-parallel corpora for machine translation (MT) from unaligned text. We use multilingual BERT to create source and target sentence…
The Multilingual Amazon Reviews Corpus
Phillip Keung, Yichao Lu, György Szarvas +1
We present the Multilingual Amazon Reviews Corpus (MARC), a large-scale collection of Amazon reviews for multilingual text classification. The corpus contains reviews in English, J…
Improving Non-autoregressive Neural Machine Translation with Monolingual Data
Jiawei Zhou, Phillip Keung
Non-autoregressive (NAR) neural machine translation is usually done via knowledge distillation from an autoregressive (AR) model. Under this framework, we leverage large monolingua…
Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings
Phillip Keung, Yichao Lu, Julian Salazar +1
Multilingual contextual embeddings have demonstrated state-of-the-art performance in zero-shot cross-lingual transfer learning, where multilingual BERT is fine-tuned on one source…
Attentional Speech Recognition Models Misbehave on Out-of-domain Utterances
Phillip Keung, Wei Niu, Yichao Lu +2
We discuss the problem of echographic transcription in autoregressive sequence-to-sequence attentional architectures for automatic speech recognition, where a model produces very l…
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER
Phillip Keung, Yichao Lu, Vikas Bhardwaj
Contextual word embeddings (e.g. GPT, BERT, ELMo, etc.) have demonstrated state-of-the-art performance on various NLP tasks. Recent work with the multilingual version of BERT has s…
A neural interlingua for multilingual machine translation
Yichao Lu, Phillip Keung, Faisal Ladhak +3
We incorporate an explicit neural interlingua into a multilingual encoder-decoder neural machine translation (NMT) architecture. We demonstrate that our model learns a language-ind…
A practical approach to dialogue response generation in closed domains
Yichao Lu, Phillip Keung, Shaonan Zhang +2
We describe a prototype dialogue response generation model for the customer service domain at Amazon. The model, which is trained in a weakly supervised fashion, measures the simil…