REVIEW 3 cited by
Learning to Write with Cooperative Discriminators
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Recurrent Neural Networks (RNNs) are powerful autoregressive sequence models, but when used to generate natural language their output tends to be overly generic, repetitive, and self-contradictory. We postulate that the objective function optimized by RNN language models, which amounts to the overall perplexity of a text, is not expressive enough to capture the notion of communicative goals described by linguistic principles such as Grice's Maxims. We propose learning a mixture of multiple discriminative models that can be used to complement the RNN generator and guide the decoding process. Human evaluation demonstrates that text generated by our system is preferred over that of baselines by a large margin and significantly enhances the overall coherence, style, and information content of the generated text.
Forward citations
Cited by 3 Pith papers
-
Decision Transformer: Reinforcement Learning via Sequence Modeling
Decision Transformer casts RL as autoregressive sequence modeling conditioned on desired returns, past states and actions, matching or exceeding offline RL baselines on Atari, Gym and Key-to-Door tasks.
-
Multi-agents based User Values Mining for Recommendation
A multi-LLM debate framework extracts Schwartz value labels from user interaction histories, and adding these labels through contrastive learning improves recommendation accuracy on PENS and MovieLens-1M.
-
Reflection-Window Decoding: Text Generation with Selective Refinement
Selectively refining uncertain windows during decoding improves text quality over greedy and beam search, with a theory formalizing why greedy decoding can miss the joint-probability-optimal response.
Discussion (0). Continue with ORCID to comment.