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--- |
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language: |
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- en |
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datasets: |
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- SAMSum |
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metrics: |
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- ROUGE-L |
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tags: |
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- Question answering |
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- T5 |
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- Declarative |
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- Text generation |
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widget: |
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- text: "q: do you have any hobbies that you like to do while you are at home ? a: watching online shows" |
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- text: "q: do you watch a lot of comedy ? a: Yes, it will help calm the mind" |
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--- |
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# Transforming Question-Answer Pairs to Full Declarative Answer Form |
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Considering the question of "Which drug did you take?" and the answer of "Doliprane", the aim of this model is to derive a full answer to the question "I took Doliprane". |
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## Model training |
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We fine-tune T5 (Raffel et al.,2019), a pre-trained encoder-decoder model, on two datasets of (question, incomplete answer, full answer) triples, one for wh- and one for yes-no(YN) questions. |
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For wh-questions, we use 3,300 entries of the dataset consisting of (question, answer, declarative answer sentence) triples gathered by Demszky et al. (2018) using Amazon Mechanical Turk workers. |
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For YN questions, we used the SAMSum corpus, (Gliwa et al., 2019) which contains short dialogs in chit-chat format. We created |
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1,100 (question, answer, full answer) triples by au- |
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tomatically extracting YN (question, answer) pairs |
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from this corpus and manually associating them |
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with the corresponding declarative answer. Data |
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was splitted into train and test (9:1) and the fine- |
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tuned model achieved 0.90 ROUGE-L score on the |
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test set. |
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## Model Details |
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Model was developed as one of the proposed modules in the following paper for dialog transformation. |
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```"F. Ghassemi Toudeshki, A. Liednikova, P Jolivet and C. Gardent, Exploring the Influence of Dialog Input Format for Unsupervised Clinical Questionnaire Filling, Proceedings of the 13th International Workshop on Health Text Mining and Information Analysis (co-located with EMNLP 2022), Abu Dhabi, 7 December 2022."``` |
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It was used to transform question-answer paris in information-seeking dialogs to declarative form and at the end to have the declarative transform of the whole dialog. |
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## Test the model |
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```python |
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!pip install transformers |
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!pip install sentencepiece |
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``` |
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```python |
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from transformers import AutoModelWithLMHead, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("Farnazgh/QA2D") |
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model = AutoModelWithLMHead.from_pretrained("Farnazgh/QA2D") |
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def transform_qa2d(question, answer, max_length=150): |
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text = "q: "+question+" a: "+answer |
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input_ids = tokenizer.encode(text, return_tensors="pt", add_special_tokens=True) |
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generated_ids = model.generate(input_ids=input_ids, num_beams=2, max_length=max_length, early_stopping=True)[0] |
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preds = tokenizer.decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) |
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return preds |
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``` |
