no code implementations • SIGDIAL (ACL) 2021 • Abigail See, Christopher Manning
We find that unclear user utterances are a major source of generative errors such as ignoring, hallucination, unclearness and repetition.
no code implementations • 28 Sep 2022 • Amelia Glaese, Nat McAleese, Maja Trębacz, John Aslanides, Vlad Firoiu, Timo Ewalds, Maribeth Rauh, Laura Weidinger, Martin Chadwick, Phoebe Thacker, Lucy Campbell-Gillingham, Jonathan Uesato, Po-Sen Huang, Ramona Comanescu, Fan Yang, Abigail See, Sumanth Dathathri, Rory Greig, Charlie Chen, Doug Fritz, Jaume Sanchez Elias, Richard Green, Soňa Mokrá, Nicholas Fernando, Boxi Wu, Rachel Foley, Susannah Young, Iason Gabriel, William Isaac, John Mellor, Demis Hassabis, Koray Kavukcuoglu, Lisa Anne Hendricks, Geoffrey Irving
We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines.
no code implementations • SIGDIAL (ACL) 2022 • Ethan A. Chi, Ashwin Paranjape, Abigail See, Caleb Chiam, Trenton Chang, Kathleen Kenealy, Swee Kiat Lim, Amelia Hardy, Chetanya Rastogi, Haojun Li, Alexander Iyabor, Yutong He, Hari Sowrirajan, Peng Qi, Kaushik Ram Sadagopan, Nguyet Minh Phu, Dilara Soylu, Jillian Tang, Avanika Narayan, Giovanni Campagna, Christopher D. Manning
We present Chirpy Cardinal, an open-domain social chatbot.
no code implementations • 27 Aug 2020 • Ashwin Paranjape, Abigail See, Kathleen Kenealy, Haojun Li, Amelia Hardy, Peng Qi, Kaushik Ram Sadagopan, Nguyet Minh Phu, Dilara Soylu, Christopher D. Manning
At the end of the competition, Chirpy Cardinal progressed to the finals with an average rating of 3. 6/5. 0, a median conversation duration of 2 minutes 16 seconds, and a 90th percentile duration of over 12 minutes.
1 code implementation • CONLL 2019 • Abigail See, Aneesh Pappu, Rohun Saxena, Akhila Yerukola, Christopher D. Manning
Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks.
2 code implementations • NAACL 2019 • Abigail See, Stephen Roller, Douwe Kiela, Jason Weston
A good conversation requires balance -- between simplicity and detail; staying on topic and changing it; asking questions and answering them.
40 code implementations • ACL 2017 • Abigail See, Peter J. Liu, Christopher D. Manning
Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text).
Ranked #12 on Extractive Text Summarization on CNN / Daily Mail
1 code implementation • CONLL 2016 • Abigail See, Minh-Thang Luong, Christopher D. Manning
Neural Machine Translation (NMT), like many other deep learning domains, typically suffers from over-parameterization, resulting in large storage sizes.