Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation (ACL 2023)

Code and data for the ACL 2023 paper: https://aclanthology.org/2023.acl-long.603.pdf

Note: All the pre-trained models can be found here.

1. About the Two Notebooks

• Notebook SG_CQG_Graph_Construction.ipynb contains the class Semantic_Graph_Constructor which constructs our proposed semantic graph integrating coreference, co-occurence, name entities information, given a context. To create a semantic graph from a context, one can create an instance of the above class and call the build_linking_graph function to get the triples. See the example in the notebook.

• Notebook SG_CQG_Generate_Conversations.ipynb contains the class Generate_Conversation_from_Graph aiming to generate a conversation, given a semantic graph and a context. It is achievable by calling 1 function. One can see the notebooks for the sample calls.

2. Pre-requisites Softwares

Please check the two notebooks for different setups.

3. Reference

Any question, please email directly to Do Xuan Long via email: [email protected].

If you have found the codes helpful, please consider to cite the paper:

@inproceedings{do-etal-2023-modeling,
    title = "Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation",
    author = "Do, Xuan Long  and
      Zou, Bowei  and
      Joty, Shafiq  and
      Tai, Tran  and
      Pan, Liangming  and
      Chen, Nancy  and
      Aw, Ai Ti",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.603",
    pages = "10785--10803",
    abstract = "Conversational Question Generation (CQG) is a critical task for machines to assist humans in fulfilling their information needs through conversations. The task is generally cast into two different settings: answer-aware and answer-unaware. While the former facilitates the models by exposing the expected answer, the latter is more realistic and receiving growing attentions recently. What-to-ask and how-to-ask are the two main challenges in the answer-unaware setting. To address the first challenge, existing methods mainly select sequential sentences in context as the rationales. We argue that the conversation generated using such naive heuristics may not be natural enough as in reality, the interlocutors often talk about the relevant contents that are not necessarily sequential in context. Additionally, previous methods decide the type of question to be generated (boolean/span-based) implicitly. Modeling the question type explicitly is crucial as the answer, which hints the models to generate a boolean or span-based question, is unavailable. To this end, we present SG-CQG, a two-stage CQG framework. For the what-to-ask stage, a sentence is selected as the rationale from a semantic graph that we construct, and extract the answer span from it. For the how-to-ask stage, a classifier determines the target answer type of the question via two explicit control signals before generating and filtering. In addition, we propose Conv-Distinct, a novel evaluation metric for CQG, to evaluate the diversity of the generated conversation from a context. Compared with the existing answer-unaware CQG models, the proposed SG-CQG achieves state-of-the-art performance.",
}