Search Results for author:
Found 11 papers, 1 papers with code
A practical approach to dialogue response generation in closed domains
We describe a prototype dialogue response generation model for the customer service domain at Amazon.
A neural interlingua for multilingual machine translation
We incorporate an explicit neural interlingua into a multilingual encoder-decoder neural machine translation (NMT) architecture.
Goal-Oriented End-to-End Conversational Models with Profile Features in a Real-World Setting
To test our models, a customer service agent handles live contacts and at each turn we present the top four model responses and allow the agent to select (and optionally edit) one of the suggestions or to type their own.
Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER
We report the magnitude of the improvement on the multilingual MLDoc text classification and CoNLL 2002/2003 named entity recognition tasks.
Attentional Speech Recognition Models Misbehave on Out-of-domain Utterances
We discuss the problem of echographic transcription in autoregressive sequence-to-sequence attentional architectures for automatic speech recognition, where a model produces very long sequences of repetitive outputs when presented with out-of-domain utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Don't Use English Dev: On the Zero-Shot Cross-Lingual Evaluation of Contextual Embeddings
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 language and evaluated on a different target language.
Best Practices for Data-Efficient Modeling in NLG:How to Train Production-Ready Neural Models with Less Data
In this paper, we present approaches that have helped us deploy data-efficient neural solutions for NLG in conversational systems to production.
Conversational Answer Generation and Factuality for Reading Comprehension Question-Answering
A popular approach to QA is extractive reading comprehension (RC) which finds an answer span in a text passage.
Lumos : Empowering Multimodal LLMs with Scene Text Recognition
We introduce Lumos, the first end-to-end multimodal question-answering system with text understanding capabilities.
Getting to Production with Few-shot Natural Language Generation Models
In this paper, we study the utilization of pre-trained language models to enable few-shotNatural Language Generation (NLG) in task-oriented dialog systems.
Building Adaptive Acceptability Classifiers for Neural NLG
We propose a novel framework to train models to classify acceptability of responses generated by natural language generation (NLG) models, improving upon existing sentence transformation and model-based approaches.
