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Found 8 papers, 4 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.
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.
