🦦 OTTers: One-turn Topic Transitions for Open-Domain Dialogue

Explanation of data collection, data splits, and experiments can be found here.

Data

You can download the data already divided in the two in-domain and out-of-domain splits.

Number of topic pairs for each data split:

• in-domain: allows one of the topics in each pair of the test set to appear in the train set, although with a different second topic,
• out-of-domain: none of the topics in the test set are present in any of the topic-pairs in the train set.

	Train	Dev	Test
in-domain	1929	1160	1158
out-of-domain	2034	1152	1130

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Models

To reproduce our results clone Multigen and follow their instructions to train the model on $\alpha$NLG, then save the trained model as anlg.
Preprocess multi-hop relational paths for OTTers. Set $DATA to either be in_domain or out_of_domain.

export DATA=in_domain
python ground_concepts_simple.py $DATA
python find_neighbours.py $DATA
python filter_triple.py $DATA

Training

export DATA_TYPE={in_domain, out_of_domain}
export ROOT_PATH=..
export DEVICE=1
export PRE_TRAINED={gpt2-small, anlg}
CUDA_VISIBLE_DEVICES=${DEVICE} \
python3 main.py \
--train_data_file ${ROOT_PATH}/data/${DATA_TYPE}/train \
--dev_data_file ${ROOT_PATH}/data/${DATA_TYPE}/dev \
--test_data_file ${ROOT_PATH}/data/${DATA_TYPE}/test \
--graph_path 2hops_100_directed_triple_filter.json \
--output_dir ${ROOT_PATH}/models/${DATA_TYPE}/grf-${DATA_TYPE} \
--source_length 32 \
--target_length 16 \
--model_type gpt2 \
--model_name_or_path ${ROOT_PATH}/models/${PRE_TRAINED} \
--do_train \
--per_gpu_train_batch_size 16 \
--per_gpu_eval_batch_size 16 \
--workers 7 \
--seed 42 \
--evaluate_metrics bleu \
--overwrite_output_dir \
--num_train_epochs 3 \
--learning_rate 3e-5 \
--aggregate_method max \
--alpha 3 \
--beta 5 \
--gamma 0.5 \
--weight_decay 0.0 \
--warmup_ratio 0.0 \
--logging_steps 20 \

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Citation

If you use the dataset in your work please cite with the following

@inproceedings{sevegnani-etal-2021-otters,
    title = "{OTT}ers: One-turn Topic Transitions for Open-Domain Dialogue",
    author = "Sevegnani, Karin  and
      Howcroft, David M.  and
      Konstas, Ioannis  and
      Rieser, Verena",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics"
}