1.3k citations · 2.4k across the 11 of their papers we have counts for
11 papers
LaMDA: Language Models for Dialog Applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall +57
We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of Transformer-based neural language models specialized for dialog, which have up to 137B parameters an…
Multilingual Universal Sentence Encoder for Semantic Retrieval
Yinfei Yang, Daniel Cer, Amin Ahmad +9
We introduce two pre-trained retrieval focused multilingual sentence encoding models, respectively based on the Transformer and CNN model architectures. The models embed text from…
Hierarchical Document Encoder for Parallel Corpus Mining
Mandy Guo, Yinfei Yang, Keith Stevens +5
We explore using multilingual document embeddings for nearest neighbor mining of parallel data. Three document-level representations are investigated: (i) document embeddings gener…
Improving Multilingual Sentence Embedding using Bi-directional Dual Encoder with Additive Margin Softmax
Yinfei Yang, Gustavo Hernandez Abrego, Steve Yuan +6
In this paper, we present an approach to learn multilingual sentence embeddings using a bi-directional dual-encoder with additive margin softmax. The embeddings are able to achieve…
Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model
Muthuraman Chidambaram, Yinfei Yang, Daniel Cer +4
A significant roadblock in multilingual neural language modeling is the lack of labeled non-English data. One potential method for overcoming this issue is learning cross-lingual t…
Effective Parallel Corpus Mining using Bilingual Sentence Embeddings
Mandy Guo, Qinlan Shen, Yinfei Yang +8
This paper presents an effective approach for parallel corpus mining using bilingual sentence embeddings. Our embedding models are trained to produce similar representations exclus…
Learning Semantic Textual Similarity from Conversations
Yinfei Yang, Steve Yuan, Daniel Cer +7
We present a novel approach to learn representations for sentence-level semantic similarity using conversational data. Our method trains an unsupervised model to predict conversati…
Universal Sentence Encoder
Daniel Cer, Yinfei Yang, Sheng-yi Kong +10
We present models for encoding sentences into embedding vectors that specifically target transfer learning to other NLP tasks. The models are efficient and result in accurate perfo…
Efficient Natural Language Response Suggestion for Smart Reply
Matthew Henderson, Rami Al-Rfou, Brian Strope +6
This paper presents a computationally efficient machine-learned method for natural language response suggestion. Feed-forward neural networks using n-gram embedding features encode…
Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models
Louis Shao, Stephan Gouws, Denny Britz +3
Sequence-to-sequence models have been applied to the conversation response generation problem where the source sequence is the conversation history and the target sequence is the r…
Conversational Contextual Cues: The Case of Personalization and History for Response Ranking
Rami Al-Rfou, Marc Pickett, Javier Snaider +3
We investigate the task of modeling open-domain, multi-turn, unstructured, multi-participant, conversational dialogue. We specifically study the effect of incorporating different e…