16 citations · 60 across the 15 of their papers we have counts for
21 papers
On the Effectiveness of Offline RL for Dialogue Response Generation
Paloma Sodhi, Felix Wu, Ethan R. Elenberg +2
A common training technique for language models is teacher forcing (TF). TF attempts to match human language exactly, even though identical meanings can be expressed in different w…
Long-term Control for Dialogue Generation: Methods and Evaluation
Ramya Ramakrishnan, Hashan Buddhika Narangodage, Mauro Schilman +2
Current approaches for controlling dialogue response generation are primarily focused on high-level attributes like style, sentiment, or topic. In this work, we focus on constraine…
Wav2Seq: Pre-training Speech-to-Text Encoder-Decoder Models Using Pseudo Languages
Felix Wu, Kwangyoun Kim, Shinji Watanabe +4
We introduce Wav2Seq, the first self-supervised approach to pre-train both parts of encoder-decoder models for speech data. We induce a pseudo language as a compact discrete repres…
Focus Attention: Promoting Faithfulness and Diversity in Summarization
Rahul Aralikatte, Shashi Narayan, Joshua Maynez +2
Professional summaries are written with document-level information, such as the theme of the document, in mind. This is in contrast with most seq2seq decoders which simultaneously…
Planning with Learned Entity Prompts for Abstractive Summarization
Shashi Narayan, Yao Zhao, Joshua Maynez +3
We introduce a simple but flexible mechanism to learn an intermediate plan to ground the generation of abstractive summaries. Specifically, we prepend (or prompt) target summaries…
Stepwise Extractive Summarization and Planning with Structured Transformers
Shashi Narayan, Joshua Maynez, Jakub Adamek +3
We propose encoder-centric stepwise models for extractive summarization using structured transformers -- HiBERT and Extended Transformers. We enable stepwise summarization by injec…