test-mass-forker-org-1 / ml-cread

CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues

Paper | Task | Dataset | Run Code | Citation | License | Contact

This is the source code of the paper CREAD: Combined Resolution of Ellipses and Anaphora in Dialogues. In this work, we propose a novel joint learning framework of modeling coreference resolution and query rewriting for complex, multi-turn dialogue understanding. The coreference resolution MuDoCo dataset augmented with our query rewrite annotation is released as well.

Task Description

Given an ongoing dialogue between a user and a dialogue assistant, for the user query, the model is required to predict both coreference links between the query and the dialogue context, and the self-contained rewritten user query that is independent to the dialogue context.

Dataset

The MuDoCo dataset is a public dataset that contains 7.5k task-oriented multi-turn dialogues across 6 domains (calling, messaging, music, news, reminders, weather). Each dialogue turn is annotated with coreference links (links field). Please refer to MuDoCo for more details.

In the MuDoCo-QR-dataset used in work, we annotate the query rewrite for each utterance, including both user and system turn. On top of the MudoCo data format, we add three fields graded, rewrite_required and rewritten_utterance. Most of the turns are with annotated with query rewrite (graded is true). Only 1.4% dialogue turns with incomplete dialogue context (e.g., missing turns) in MuDoCo are filtered out (graded is false). rewrite_required records whether the input utterance should be rewritten or not. rewritten_utterance is the rewritten query, same as the utterance if rewrite_required is false.

The resulting dataset is provided in the folder MuDoCo-QR-dataset.

{
    "number": 3,
    "utterance": "Show me a live version that he moonwalks on .",
    "links": [
        [
            {
                "turn_id": 1,
                "text": "Michael Jackson",
                "span": {
                    "start": 5,
                    "end": 20
                }
            },
            {
                "turn_id": 3,
                "text": "he",
                "span": {
                    "start": 28,
                    "end": 30
                }
            }
        ]
    ],
    "graded": true,
    "rewritten_utterance": "Show me a live version that Michael Jackson moonwalks on",
    "rewrite_required": true
}

Requirements

python3.6 and the packages in requirements.txt, install them by running

>>> pip install -r requirements.txt

Run the Code

Enter the modeling folder and follow the instruction below.

>>> cd modeling

Data Pre-processing

First run the following command to prepare the data for training.

The processed data will be stored in the proc_data/ directory.

>>> python utils/process_data.py

Training

Run train.sh to train the model, which calls main.py with default hyper-parameters.

>>> bash train.sh [job_name]

The model checkpoint will be stored at checkpoint/$job_name, and training log file is at log/$job_name.log

A reference training log (log/trained-cread.log) is provided.

Evaluation

Run decode.sh to decode using a trained model. job_name is the same as specified in training.

>>> bash decode.sh [job_name]

Evaluation result, with both generated rewritten utterances and model performance, is recorded in deocde/$job_name.json.

A reference decoding file (decode/trained-cread.json) is provided.

Key Hyper-parameters in Main.py

• task: which task to perform. The default value qr-coref specifies our complete joint learning model. Set to qr for the model variant qr-only model or coref for the model variant coref-only model.

• coref_layer_idx: which gpt2 layers to use for coreference resolution, e.g., "1,5,11" uses three layers. n is between 0 to 11, if default gpt2-small is used.

• n_coref_head: how many attention heads to use in each layer for coreference resolution. n is between 1 to 12.

• use_coref_attn: whether to use coref2qr attention mechanism.

• use_binary_cls: whether to use binary rewriting classifier.

More detailed explanation of other arguments can be found in utils/utils.py.

Citation

@inproceedings{tseng-etal-2021-cread,
    title = "{CREAD}: Combined Resolution of Ellipses and Anaphora in Dialogues",
    author = "Tseng, Bo-Hsiang  and
      Bhargava, Shruti  and
      Lu, Jiarui  and
      Moniz, Joel Ruben Antony  and
      Piraviperumal, Dhivya  and
      Li, Lin  and
      Yu, Hong",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.naacl-main.265",
    pages = "3390--3406",
}

License

The code in this repository is licensed according to the LICENSE file.

Contact Us

Please contact [email protected] or [email protected], or raise an issue in this repository.