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System Initiative Prediction (SIP)

This is the code repository for the paper titled System Initiative Prediction for Multi-turn Conversational Information Seeking, which got accepted at The 32nd ACM International Conference on Information and Knowledge Management (CIKM 2023) as a long paper.

We kindly ask you to cite our papers if you find this repository useful:

@inproceedings{meng2023system,
  title={System Initiative Prediction for Multi-turn Conversational Information Seeking},
  author={Meng, Chuan and Aliannejadi, Mohammad and de Rijke, Maarten},
  booktitle={Proceedings of the 32nd ACM International Conference on Information and Knowledge Management},
  pages={1807--1817},
  year={2023}
}

To reproduce the reported results in the paper, please follow the instructions outlined below:

Prerequisites

Install dependencies:

pip install -r requirements.txt

Data Preprocessing

The following commands are used to conduct data preprocessing, which includes the automatic annotation of initiative-taking decision labels. We derive the initiative annotations by mapping the manual annotations of actions to initiative or non-initiative labels. The raw data for the WISE, MSDialog and ClariQ is stored in the paths ./data/WISE/, ./data/MSDialog/ and ./data/ClariQ/. The preprocessed data would be still stored in these paths.

Run the following commands to preprocess WISE:

python -u ./dataset/preprocess_WISE.py \
--input_path ./dataset/WISE/conversation_train_line.json \
--output_path ./dataset/WISE/train_WISE.pkl

python -u ./dataset/preprocess_WISE.py \
--input_path ./dataset/WISE/conversation_valid_line.json \
--output_path ./dataset/WISE/valid_WISE.pkl

python -u ./dataset/preprocess_WISE.py \
--input_path ./dataset/WISE/conversation_test_line.json \
--output_path ./dataset/WISE/test_WISE.pkl

Run the following commands to preprocess MSDialog:

python -u ./dataset/preprocess_MSDialog.py \
--input_path ./dataset/MSDialog/train.tsv \
--output_path ./dataset/MSDialog/train_MSDialog.pkl

python -u ./dataset/preprocess_MSDialog.py \
--input_path ./dataset/MSDialog/valid.tsv \
--output_path ./dataset/MSDialog/valid_MSDialog.pkl

python -u ./dataset/preprocess_MSDialog.py \
--input_path ./dataset/MSDialog/test.tsv \
--output_path ./dataset/MSDialog/test_MSDialog.pkl

Run the following commands to preprocess ClariQ:

python -u ./dataset/preprocess_ClariQ.py \
--input_path ./dataset/ClariQ/train.tsv \
--output_path ./dataset/ClariQ/train_ClariQ.pkl

python -u ./dataset/preprocess_ClariQ.py \
--input_path ./dataset/ClariQ/dev.tsv \
--output_path ./dataset/ClariQ/valid_ClariQ.pkl

python -u ./dataset/preprocess_ClariQ.py \
--input_path ./dataset/ClariQ/test_with_labels.tsv \
--output_path ./dataset/ClariQ/test_ClariQ.pkl

Run SIP

LLaMA

We provide the script for running LLaMA; see here.

Before running the script, please make sure your Cuda version is greater than or equal to 11.1. Next, download the LLaMA original checkpoints and convert them to the Hugging Face Transformers format; see here for more details.

Because the original LLaMA performs extremely badly on WISE, which is in Chinese text, we use the Chinese versions of LLaMA from here on WISE. Please follow the instruction in the link to produce Chinese LLaMA checkpoints. At the time of writing, the Chinese versions of LLaMA only have Chinese-LLaMA-7B, Chinese-LLaMA-13B, Chinese-LLaMA-Plus-7B and Chinese-LLaMA-Plus-13B. In particular, we choose to use the plus versions, namely Chinese-LLaMA-Plus-7B and Chinese-LLaMA-Plus-13B, because the plus versions were trained on more data and are highly recommended for use by the releaser.

Our preliminary experiments showed that all LLaMA variants on the two datasets perform best when injected with 2 complete conversations randomly sampled from the training set, given the same random seed.

WISE

Run the following commands to run LLaMA on WISE:

python -u ./model/LLaMA.py \
--model LLaMA-zh-7B-plus \
--pretained {your local path to the checkpoint of Chinese LLaMA-7B-plus} \
--demonstration_path ./dataset/WISE/train_WISE.pkl \
--input_path ./dataset/WISE/test_WISE.pkl \
--output_path ./output/ \
--max_new_tokens 5 \
--batch_size 2 \
--demonstration_num 2

python -u ./model/LLaMA.py \
--model LLaMA-zh-13B-plus
--pretained {your local path to the checkpoint of Chinese LLaMA-13B-plus} \
--demonstration_path ./dataset/WISE/train_WISE.pkl \
--input_path ./dataset/WISE/test_WISE.pkl \
--output_path ./output/ \
--max_new_tokens 5 \
--batch_size 2 \
--demonstration_num 2

The inference output files would be saved in the paths .\output\WISE.SIP.LLaMA-zh-7B-plus and .\output\WISE.SIP.LLaMA-zh-13B-plus.

MSDialog

Run the following commands to run LLaMA on MSDialog:

python -u ./model/LLaMA.py \
--model LLaMA-7B \
--pretained {your local path to the checkpoint of LLaMA-7B} \
--demonstration_path ./dataset/MSDialog/train_MSDialog.pkl \
--input_path ./dataset/MSDialog/test_MSDialog.pkl \
--output_path ./output/ \
--max_new_tokens 10 \
--batch_size 4 \
--demonstration_num 2

python -u ./model/LLaMA.py \
--model LLaMA-13B \
--pretained {your local path to the checkpoint of LLaMA-13B} \
--demonstration_path ./dataset/MSDialog/train_MSDialog.pkl \
--input_path ./dataset/MSDialog/test_MSDialog.pkl \
--output_path ./output/ \
--max_new_tokens 10 \
--batch_size 2 \
--demonstration_num 2

python -u ./model/LLaMA.py \
--model LLaMA-30B \
--pretained {your local path to the checkpoint of LLaMA-30B} \
--demonstration_path ./dataset/MSDialog/train_MSDialog.pkl \
--input_path ./dataset/MSDialog/test_MSDialog.pkl \
--output_path ./output/ \
--max_new_tokens 10 \
--batch_size 1 \
--demonstration_num 2

python -u ./model/LLaMA.py \
--model LLaMA-65B \
--pretained {your local path to the checkpoint of LLaMA-65B} \
--demonstration_path ./dataset/MSDialog/train_MSDialog.pkl \
--input_path ./dataset/MSDialog/test_MSDialog.pkl \
--output_path ./output/ \
--max_new_tokens  10 \
--batch_size 1 \
--demonstration_num 2

The inference output files would be saved in the paths .\output\MSDialog.SIP.LLaMA-7B, MSDialog.SIP.LLaMA-13B, MSDialog.SIP.LLaMA-30B and MSDialog.SIP.LLaMA-65B.

MuSIc

WISE

Train MuSIc on the training set of WISE and conduct inference on the validation and test sets of WISE:

python -u ./model/Run.py \
--task SIP \
--model DistanceCRF \
--input_path ./dataset/WISE/train_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode train

python -u ./model/Run.py \
--task SIP \
--model DistanceCRF \
--input_path ./dataset/WISE/valid_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference

python -u ./model/Run.py \
--task SIP \
--model DistanceCRF \
--input_path ./dataset/WISE/test_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference

The above commands would produce model checkpoints and inference output files, which are stored in the paths ./output/WISE.SIP.DistanceCRF/checkpoints/ and ./output/WISE.SIP.DistanceCRF/, respectively.

MSDialog

Train MuSIc on the training set of MSDialog and conduct inference on the validation and test sets of MSDialog:

python -u ./model/Run.py \
--task SIP \
--model DistanceCRF \
--input_path ./dataset/MSDialog/train_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode train

python -u ./model/Run.py \
--task SIP \
--model DistanceCRF \
--input_path ./dataset/MSDialog/valid_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference

python -u ./model/Run.py \
--task SIP \
--model DistanceCRF \
--input_path ./dataset/MSDialog/test_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference

The above commands would produce model checkpoints and inference output files, which are stored in the paths ./output/MSDialog.SIP.DistanceCRF/checkpoints/ and ./output/MSDialog.SIP.DistanceCRF/, respectively.

Run clarification need prediction

Run the following command to directly train MuSIc on the training set of ClariQ and conduct inference on the validation and test sets of ClariQ:

python -u ./model/Run.py \
--task SIP \
--model DistanceCRF \
--input_path ./dataset/ClariQ/train_ClariQ.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode train \

python -u ./model/Run.py \
--task SIP \
--model DistanceCRF \
--input_path ./dataset/ClariQ/valid_ClariQ.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference \

python -u ./model/Run.py \
--task SIP \
--model DistanceCRF \
--input_path ./dataset/ClariQ/test_ClariQ.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference \

The above commands would produce model checkpoints and inference output files, which are saved in the paths ./output/ClariQ.SIP.DistanceCRF/checkpoints/ and ./output/ClariQ.SIP.DistanceCRF/, respectively.

Run the following command to fine-tune MuSIc (pre-trained on SIP on the training set of MSDialog) on the training set of ClariQ and conduct inference on the validation and test sets of ClariQ:

python -u ./model/Run.py \
--task SIP \
--model DistanceCRF \
--input_path ./dataset/ClariQ/train_ClariQ.pkl \
--output_path ./output/ \
--log_path ./log/ \
--initialization_path {your local path to the checkpoint trained on SIP on MSDialog} \
--mode train \

python -u ./model/Run.py \
--task SIP \
--model DistanceCRF \
--input_path ./dataset/ClariQ/valid_ClariQ.pkl \
--output_path ./output/ \
--log_path ./log/ \
--initialization_path {your local path to the checkpoint trained on SIP on MSDialog} \
--mode inference \

python -u ./model/Run.py \
--task SIP \
--model DistanceCRF \
--input_path ./dataset/ClariQ/test_ClariQ.pkl \
--output_path ./output/ \
--log_path ./log/ \
--initialization_path {your local path to the checkpoint trained on SIP on MSDialog} \
--mode inference \

Please specify --initialization_path, which shows your local path to the checkpoint trained on SIP on MSDialog. The above commands would produce checkpoints, which would be saved in the paths ./output/ClariQ.SIP.DistanceCRF-TransferLearning/checkpoints/; the inference output files would be saved in the path ./output/ClariQ.SIP.DistanceCRF-TransferLearning/.

Evaluate SIP and clarification need prediction

Evaluate LLaMA on the test set of WISE:

python -u Evaluation.py \
--prediction_path ./output/WISE.SIP.LLaMA-zh-7B-plus \
--label_path ./dataset/WISE/test_WISE.pkl

python -u Evaluation.py \
--prediction_path ./output/WISE.SIP.LLaMA-zh-13B-plus \
--label_path ./dataset/WISE/test_WISE.pkl

The files recording the evaluation results would be saved in the paths ./output/WISE.SIP.LLaMA-zh-7B-plus/ and ./output/WISE.SIP.LLaMA-zh-13B-plus/.

Evaluate LLaMA on the test set of MSDialog:

python -u Evaluation.py \
--prediction_path ./output/MSDialog.SIP.LLaMA-7B \
--label_path ./dataset/MSDialog/test_MSDialog.pkl

python -u Evaluation.py \
--prediction_path ./output/MSDialog.SIP.LLaMA-13B \
--label_path ./dataset/MSDialog/test_MSDialog.pkl

python -u Evaluation.py \
--prediction_path ./output/MSDialog.SIP.LLaMA-30B \
--label_path ./dataset/MSDialog/test_MSDialog.pkl

python -u Evaluation.py \
--prediction_path ./output/MSDialog.SIP.LLaMA-65B \
--label_path ./dataset/MSDialog/test_MSDialog.pkl

The files recording the evaluation results would be saved in the paths ./output/MSDialog.SIP.LLaMA-7B/, ./output/MSDialog.SIP.LLaMA-13B/, ./output/MSDialog.SIP.LLaMA-30B/ and ./output/MSDialog.SIP.LLaMA-65B/.

Evaluate MuSIc on the validation and test sets of WISE:

python -u Evaluation.py \
--prediction_path ./output/WISE.SIP.DistanceCRF \
--label_path ./dataset/WISE/valid_WISE.pkl

python -u Evaluation.py \
--prediction_path ./output/WISE.SIP.DistanceCRF \
--label_path ./dataset/WISE/test_WISE.pkl

The files recording the evaluation results would be saved in the path ./output/WISE.SIP.DistanceCRF/.

Evaluate MuSIc on the validation and test sets of MSDialog:

python -u Evaluation.py \
--prediction_path ./output/MSDialog.SIP.DistanceCRF \
--label_path ./dataset/MSDialog/valid_MSDialog.pkl

python -u Evaluation.py \
--prediction_path ./output/MSDialog.SIP.DistanceCRF \
--label_path ./dataset/MSDialog/test_MSDialog.pkl

The files recording the evaluation results would be saved in the path ./output/MSDialog.SIP.DistanceCRF/.

Evaluate MuSIc (without pre-training on SIP) on the validation and test sets of ClariQ:

python -u Evaluation.py \
--prediction_path ./output/ClariQ.SIP.DistanceCRF \
--label_path ./dataset/ClariQ/valid_ClariQ.pkl

python -u Evaluation.py \
--prediction_path ./output/ClariQ.SIP.DistanceCRF \
--label_path ./dataset/ClariQ/test_ClariQ.pkl

The files recording the evaluation results would be saved in the path ./output/ClariQ.SIP.DistanceCRF/.

Evaluate MuSIc (with pre-training on SIP) on the validation and test sets of ClariQ:

python -u Evaluation.py \
--prediction_path ./output/ClariQ.SIP.DistanceCRF-TransferLearning \
--label_path ./dataset/ClariQ/valid_ClariQ.pkl

python -u Evaluation.py \
--prediction_path ./output/ClariQ.SIP.DistanceCRF-TransferLearning \
--label_path ./dataset/ClariQ/test_ClariQ.pkl

The files recording the evaluation results would be saved in the path ./output/ClariQ.SIP.DistanceCRF-TransferLearning/.

Run action prediction

Multi-label classification

WISE

Train the action prediction model based on multi-label classification on the training set of WISE and conduct inference on the validation and test sets of WISE:

python -u ./model/Run.py \
--task AP \
--model mlc \
--input_path ./dataset/WISE/train_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode train

python -u ./model/Run.py \
--task AP \
--model mlc \
--input_path ./dataset/WISE/valid_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference

python -u ./model/Run.py \
--task AP \
--model mlc \
--input_path ./dataset/WISE/test_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference

The above commands would produce model checkpoints and inference output files, which are stored in the paths ./output/WISE.AP.mlc/checkpoints/ and ./output/WISE.AP.mlc/, respectively.

MSDialog

Train the action prediction model based on multi-label classification on the training set of MSDialog and conduct inference on the validation and test sets of MSDialog:

python -u ./model/Run.py \
--task AP \
--model mlc \
--input_path ./dataset/MSDialog/train_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode train

python -u ./model/Run.py \
--task AP \
--model mlc \
--input_path ./dataset/MSDialog/valid_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference

python -u ./model/Run.py \
--task AP \
--model mlc \
--input_path ./dataset/MSDialog/test_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference

The above commands would produce model checkpoints and inference output files, which are stored in the paths ./output/MSDialog.AP.mlc/checkpoints/ and ./output/MSDialog.AP.mlc/, respectively.

SIP+multi-label classification

WISE

Train the SIP-aware action prediction model based on multi-label classification on the training set of WISE and conduct inference on the validation and test sets of WISE:

python -u ./model/Run.py \
--task SIP-AP \
--model mlc \
--input_path ./dataset/WISE/train_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode train

python -u ./model/Run.py \
--task SIP-AP \
--model mlc \
--input_path ./dataset/WISE/valid_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--SIP_path {Your local path to MuSIc's best inference output file on SIP} \
--mode inference

python -u ./model/Run.py \
--task SIP-AP \
--model mlc \
--input_path ./dataset/WISE/test_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--SIP_path {Your local path to MuSIc's best inference output file on SIP} \
--mode inference

Note that before conducting inference, please specify --SIP_path, which shows the path to MuSIc's best inference output file on SIP. E.g., for conducting inference on the test set of WISE, please specify MuSIc's best inference output file on the test set of WISE on the SIP task. The above commands would produce model checkpoints and inference output files, which are stored in the paths ./output/WISE.SIP-AP.mlc/checkpoints/ and ./output/WISE.SIP-AP.mlc/, respectively.

MSDialog

Train the SIP-aware action prediction model based on multi-label classification on the training set of MSDialog and conduct inference on the validation and test sets of MSDialog:

python -u ./model/Run.py \
--task SIP-AP \
--model mlc \
--input_path ./dataset/MSDialog/train_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode train

python -u ./model/Run.py \
--task SIP-AP \
--model mlc \
--input_path ./dataset/MSDialog/valid_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--SIP_path {Your local path to MuSIc's best inference output file on SIP} \
--mode inference

python -u ./model/Run.py \
--task SIP-AP \
--model mlc \
--input_path ./dataset/MSDialog/test_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--SIP_path {Your local path to MuSIc's best inference output file on SIP} \
--mode inference

Note that before conducting inference, please specify --SIP_path, which shows the path to MuSIc's best inference output file on SIP. E.g., for conducting inference on the test set of MSDialog, please specify MuSIc's best inference output file on the test set of MSDialog on the SIP task. The above commands would produce model checkpoints and inference output files, which are stored in the paths ./output/MSDialog.SIP-AP.mlc/checkpoints/ and ./output/MSDialog.SIP-AP.mlc/, respectively.

Sequence generation

WISE

Train the action prediction model based on sequence generation on the training set of WISE and conduct inference on the validation and test sets of WISE:

python -u ./model/Run.py \
--task AP \
--model sg \
--input_path ./dataset/WISE/train_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode train

python -u ./model/Run.py \
--task AP \
--model sg \
--input_path ./dataset/WISE/valid_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference

python -u ./model/Run.py \
--task AP \
--model sg \
--input_path ./dataset/WISE/test_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference

The above commands would produce model checkpoints and inference output files stored in the paths ./output/WISE.AP.sg/checkpoints/ and ./output/WISE.AP.sg/, respectively.

MSDialog

Train the action prediction model based on sequence generation on the training set of MSDialog and conduct inference on the validation and test sets of MSDialog:

python -u ./model/Run.py \
--task AP \
--model sg \
--input_path ./dataset/MSDialog/train_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode train

python -u ./model/Run.py \
--task AP \
--model sg \
--input_path ./dataset/MSDialog/valid_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference

python -u ./model/Run.py \
--task AP \
--model sg \
--input_path ./dataset/MSDialog/test_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode inference

The above commands would produce model checkpoints and inference output files, which are stored in the paths ./output/MSDialog.AP.sg/checkpoints/ and ./output/MSDialog.AP.sg/, respectively.

SIP+sequence generation

WISE

Train the SIP-aware action prediction model based on sequence generation on the training set of WISE and conduct inference on the validation and test sets of WISE:

python -u ./model/Run.py \
--task SIP-AP \
--model sg \
--input_path ./dataset/WISE/train_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode train

python -u ./model/Run.py \
--task SIP-AP \
--model sg \
--input_path ./dataset/WISE/valid_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--SIP_path {Your local path to MuSIc's inference output file on SIP} \
--mode inference

python -u ./model/Run.py \
--task SIP-AP \
--model sg \
--input_path ./dataset/WISE/test_WISE.pkl \
--output_path ./output/ \
--log_path ./log/ \
--SIP_path {Your local path to MuSIc's inference output file on SIP} \
--mode inference

The above commands would produce model checkpoints and inference output files, which are stored in the paths ./output/WISE.SIP-AP.sg/checkpoints/ and ./output/WISE.SIP-AP.sg/, respectively.

MSDialog

Train the SIP-aware action prediction model based on sequence generation on the training set of MSDialog and conduct inference on the validation and test sets of MSDialog:

python -u ./model/Run.py \
--task SIP-AP \
--model sg \
--input_path ./dataset/MSDialog/train_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--mode train

python -u ./model/Run.py \
--task SIP-AP \
--model sg \
--input_path ./dataset/MSDialog/valid_MSDialog \
--output_path ./output/ \
--log_path ./log/ \
--SIP_path {Your local path to MuSIc's inference output file on SIP} \
--mode inference

python -u ./model/Run.py \
--task SIP-AP \
--model sg \
--input_path ./dataset/MSDialog/test_MSDialog.pkl \
--output_path ./output/ \
--log_path ./log/ \
--SIP_path {Your local path to MuSIc's inference output file on SIP} \
--mode inference

The above commands would produce model checkpoints and inference output files, which are stored in the paths ./output/MSDialog.SIP-AP.sg/checkpoints/ and ./output/MSDialog.SIP-AP.sg/, respectively.

Evaluate action prediction

Evaluate the action prediction model based on multi-label classification on the validation and test sets of WISE:

python -u Evaluation.py \
--prediction_path ./output/WISE.AP.mlc \
--label_path ./dataset/WISE/valid_WISE.pkl

python -u Evaluation.py \
--prediction_path ./output/WISE.AP.mlc \
--label_path ./dataset/WISE/test_WISE.pkl

The files recording the evaluation results would be saved in the path ./output/WISE.AP.mlc/.

Evaluate the action prediction model based on multi-label classification on the validation and test sets of MSDialog:

python -u Evaluation.py \
--prediction_path ./output/MSDialog.AP.mlc \
--label_path ./dataset/MSDialog/valid_MSDialog.pkl

python -u Evaluation.py \
--prediction_path ./output/MSDialog.AP.mlc \
--label_path ./dataset/MSDialog/test_MSDialog.pkl

The files recording the evaluation results would be saved in the path ./output/MSDialog.AP.mlc/.

Evaluate the SIP-aware action prediction model based on multi-label classification on the validation and test sets of WISE:

python -u Evaluation.py \
--prediction_path ./output/WISE.SIP-AP.mlc \
--label_path ./dataset/WISE/valid_WISE.pkl

python -u Evaluation.py \
--prediction_path ./output/WISE.SIP-AP.mlc \
--label_path ./dataset/WISE/test_WISE.pkl

The files recording the evaluation results would be saved in the path ./output/WISE.SIP-AP.mlc/.

Evaluate the SIP-aware action prediction model based on multi-label classification on the validation and test sets of MSDialog:

python -u Evaluation.py \
--prediction_path ./output/MSDialog.SIP-AP.mlc \
--label_path ./dataset/MSDialog/valid_MSDialog.pkl

python -u Evaluation.py \
--prediction_path ./output/MSDialog.SIP-AP.mlc \
--label_path ./dataset/MSDialog/test_MSDialog.pkl

The files recording the evaluation results would be saved in the path ./output/MSDialog.SIP-AP.mlc/.

Evaluate the action prediction model based on sequence generation on the validation and test sets of WISE:

python -u Evaluation.py \
--prediction_path ./output/WISE.AP.sg \
--label_path ./dataset/WISE/valid_WISE.pkl

python -u Evaluation.py \
--prediction_path ./output/WISE.AP.sg \
--label_path ./dataset/WISE/test_WISE.pkl

The files recording the evaluation results would be saved in the path ./output/WISE.AP.sg/.

Evaluate the action prediction model based on sequence generation on the validation and test sets of MSDialog:

python -u Evaluation.py \
--prediction_path ./output/MSDialog.AP.sg \
--label_path ./dataset/MSDialog/valid_MSDialog.pkl

python -u Evaluation.py \
--prediction_path ./output/MSDialog.AP.sg \
--label_path ./dataset/MSDialog/test_MSDialog.pkl

The files recording the evaluation results would be saved in the path ./output/MSDialog.AP.sg/.

Evaluate the SIP-aware action prediction model based on sequence generation on the validation and test sets of WISE:

python -u Evaluation.py \
--prediction_path ./output/WISE.SIP-AP.sg \
--label_path ./dataset/WISE/valid_WISE.pkl

python -u Evaluation.py \
--prediction_path ./output/WISE.SIP-AP.sg \
--label_path ./dataset/WISE/test_WISE.pkl

The files recording the evaluation results would be saved in the path ./output/WISE.SIP-AP.sg/.

Evaluate the SIP-aware action prediction model based on sequence generation on the validation and test sets of MSDialog:

python -u Evaluation.py \
--prediction_path ./output/MSDialog.SIP-AP.sg \
--label_path ./dataset/MSDialog/valid_MSDialog.pkl

python -u Evaluation.py \
--prediction_path ./output/MSDialog.SIP-AP.sg \
--label_path ./dataset/MSDialog/test_MSDialog.pkl

The files recording the evaluation results would be saved in the path ./output/MSDialog.SIP-AP.sg/.

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Code for the CIKM 2023 long paper: System Initiative Prediction for Multi-turn Conversational Information Seeking

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conversational-search large-language-models

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