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Found 9 papers, 5 papers with code
Understanding and predicting user dissatisfaction in a neural generative chatbot
We find that unclear user utterances are a major source of generative errors such as ignoring, hallucination, unclearness and repetition.
Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation
Radiology reports are an instrumental part of modern medicine, informing key clinical decisions such as diagnosis and treatment.
Improving alignment of dialogue agents via targeted human judgements
We present Sparrow, an information-seeking dialogue agent trained to be more helpful, correct, and harmless compared to prompted language model baselines.
Neural Generation Meets Real People: Building a Social, Informative Open-Domain Dialogue Agent
We present Chirpy Cardinal, an open-domain social chatbot.
Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations
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.
Do Massively Pretrained Language Models Make Better Storytellers?
Large neural language models trained on massive amounts of text have emerged as a formidable strategy for Natural Language Understanding tasks.
What makes a good conversation? How controllable attributes affect human judgments
A good conversation requires balance -- between simplicity and detail; staying on topic and changing it; asking questions and answering them.
Get To The Point: Summarization with Pointer-Generator Networks
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
Compression of Neural Machine Translation Models via Pruning
Neural Machine Translation (NMT), like many other deep learning domains, typically suffers from over-parameterization, resulting in large storage sizes.
