Long Short-Term Memory Neural Networks for Artificial Dialogue Generation
Bibliographic record
Abstract
This paper investigates both of user and system modeling to extend an existing corpus of human-machine dialogue data with simulated/artificial dialogues. To simulate and generate such artificial dialogues, a long-short term memory (LSTM) neural network system is proposed. The LSTM neural network is an Encoder-Decoder built on a bidirectional multilayer architecture where the input sequence to the encoder is a list of user dialogue acts and the decoder output sequence is a list of system dialogue acts. All dialogue acts are defined at the intent level and are extracted from the TownInfo corpus for tourist information provided by the FP7 CLASSiC Project funded by European Union. The proposed LSTM configuration is compared to a fully connected Hidden Markov Model (HMM) based architecture where the states are the user dialogues acts and the observations are the system dialogue acts. After carrying out different experiments, the results obtained on the TownInfo corpus showed that the LSTM-based system outperforms the HMM-based system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".