Transformer Decoder Based Reinforcement Learning Approach for Conversational Response Generation
Bibliographic record
Abstract
Developing a machine that can hold an engaging conversation with a human is one of the main challenges in designing a dialogue system in the field of natural language processing. Responses generated by neural conversational models with log-likelihood training methods tend to lack informativeness and diversity. We address the limitation of log-likelihood training in dialogue generation models, and we present the Reinforce Transformer decoder model, our new approach for training the Transformer decoder based conversational model, which incorporates proximal policy optimization techniques from re-inforcement learning with the Transformer decoder architecture. We specifically examine the use of our proposed model for multi-turn dialogue response generation in a real word human to a human dataset. To verify the effectiveness of our proposed framework, we evaluate our model on the Reddit dialogues data, which is a real word human to a human dataset. Experiments show that our proposed response generating model in a dialogue achieves significant improvement over recurrent sequence-to-sequence models and also the state of the art Transformer based dialogue generation models based on diversity and relevance evaluation metrics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".