| --- |
| license: mit |
| language: |
| - en |
| tags: |
| - dialogue |
| - task-oriented-dialogue |
| - sentence-embeddings |
| - contextual-representations |
| library_name: pytorch |
| --- |
| |
| # TRACE — contextual turn encoders (REPRO campaign) |
|
|
| Checkpoints for **TRACE**, a contextual turn encoder that *learns* the update rule |
| applied to frozen turn embeddings in task-oriented dialogue. |
|
|
| A frozen base encoder $f_1$ maps each utterance to a static vector $e_t$; TRACE |
| ($f_2$) is a Transformer whose **tokens are turns**, and it maps the sequence |
| $(e_1,\dots,e_T)$ to contextual representations $h_t$. Training is |
| self-supervised and requires no functional annotation. |
|
|
| All checkpoints come from the **REPRO campaign**: a pre-specified protocol in |
| which dialogues are partitioned by *near-duplicate cluster*, probes are fit on a |
| held-out development split, and test labels never inform training, checkpoint or |
| hyperparameter selection. Every model was retrained from scratch under that |
| partition. |
|
|
| ## Which one to use |
|
|
| | | | |
| |---|---| |
| | **Canonical model** | `trace-repro-lite-ar-s42/best` | |
| | Base encoder (frozen) | `sergioburdisso/dialog2flow-joint-bert-base` | |
| | Attention mode | autoregressive — the deployable one | |
| | Parameters | 44.9M (6 layers, 8 heads, 768-dim) | |
|
|
| The AR mode is the one to use in practice: it only attends to the dialogue so |
| far. Bidirectional checkpoints see the whole window and are included for the |
| representation-vs-anticipation analysis. |
|
|
| ## Naming |
|
|
| ``` |
| trace-repro-<recipe>-<mode>-s<seed>[-xlc][-scratch] |
| │ │ │ │ └── trained from scratch on the 28M curriculum |
| │ │ │ └───────── continued pretraining on ~28M turns |
| │ │ └──────────────── training seed: 42, 7, 123 |
| │ └─────────────────────── ar | bidi |
| └──────────────────────────────── lite (6 layers) | deep (12) | gru (recurrent control) |
| ``` |
|
|
| Base-ablation checkpoints carry the base in the name instead: `mpnet`, `todbert`. |
|
|
| ## Correspondence with the paper |
|
|
| | checkpoints | what they support | |
| |---|---| |
| | `trace-repro-{lite,deep}-{ar,bidi}-s{42,7,123}` | main ladder: current- and next-act prediction | |
| | `trace-repro-gru-ar-s{42,7,123}` | learned recurrent control — isolates attention from recurrence | |
| | `trace-repro-{mpnet,todbert}-lite-ar-s{42,7,123}` | three-base ablation | |
| | `trace-repro-lite-ar-s*-xlc`, `-xlc-scratch` | data-scale study (28M-turn curriculum) | |
| | `trace-repro-deep-ar-s*-xlc` | model + data scale combined | |
|
|
| Untrained controls are not published: they are randomly initialized copies of the |
| same architectures, reproducible from the configs with seeds 0–4. |
|
|
| ## Contents of each folder |
|
|
| ``` |
| <checkpoint>/ |
| ├── best/ # selected by validation loss — use this one |
| │ ├── config.json |
| │ └── model.safetensors |
| ├── config.json # final epoch |
| ├── model.safetensors |
| └── trainlog.jsonl # per-epoch train/val loss |
| ``` |
|
|
| ## Usage |
|
|
| ```python |
| from contextual_turn_embeddings import ContextualTurnModelV2, encode_dialogues |
| |
| model = ContextualTurnModelV2.from_pretrained("best", device="cpu").eval() |
| # embeddings: (n_turns, 768) from the frozen base, in dialogue order |
| H, meta = encode_dialogues(model, frames, embeddings=E, device="cpu") |
| ``` |
|
|
| The package lives at |
| [`packages/contextual-turn-embeddings`](https://github.com/jumafernandez/doctorado-unsl) |
| in the project monorepo, together with the partition, the training and evaluation |
| scripts, the seeds and the commands that regenerate every table. |
|
|
| ## Base turn embeddings |
|
|
| The precomputed $e_t$ vectors used as input are published separately as datasets: |
| `jumafernandez/d2f-turn-embeddings-*`. |
| |
| ## Citation |
| |
| Fernández, J. M., Errecalde, M., & Burdisso, S. *TRACE: Learning the Update for |
| Contextual Turn Representations in Task-Oriented Dialogue.* (under review) |
| |
