Papers with neural text generation methods
What makes a good conversation? How controllable attributes affect human judgments (N19-1)
Copied to clipboard
| Challenge: | Existing work on dialogue models for conversational quality is incompletely understanding the relationship between quality and individual attributes. |
| Approach: | They propose to use conditional training and weighted decoding to control four attributes for chit-chat dialogue: repetition, specificity, response-relatedness and question-asking. |
| Outcome: | The proposed methods improve human quality judgments by controlling combinations of these variables. |
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Recent advances in generative language models have enabled machines to generate realistic texts. |
| Approach: | They propose a benchmark environment to test the 'Turing Test' problem for neural text generation methods. |
| Outcome: | The proposed benchmark environment is based on 200K human- or machine-generated samples across 20 labels Human, GPT-1, GTP-2_small, GTT-2_medium, GPG-2_large, GGT-2_PyTorch, GGP-3, GROVER_base, griover_large and GRover_mega. |