Thailand

We propose an explainable inference approach for science questions by reasoning on grounding and abstract inference chains. This paper frames question answering as a natural language abductive reasoning problem, constructing plausible explanations for each candidate answer and then selecting the candidate with the best explanation as the final answer. Our method, ExplanationLP, elicits explanations by constructing a weighted graph of relevant facts for each candidate answer and employ a linear programming formalism designed to select the optimal subgraph of explanatory facts. The graphs&#39; weighting function is composed of a set of parameters targeting relevance, cohesion and diversity, which we fine-tune for answer selection via Bayesian Optimisation. We carry out our experiments on the WorldTree and ARC-Challenge datasets to empirically demonstrate the following contributions: (1) ExplanationLP obtains strong performance when compared to transformer-based and multi-hop approaches despite having a significantly lower number of parameters (2) We illustrate that our model is able to generate plausible explanations for answer prediction
(3) Our model demonstrates better robustness towards semantic drift when compared to transformer-based and multi-hop approaches

ACL-IJCNLP 2021

Explainable Inference Over Grounding-Abstract Chains for Science Questions

constrain programming

parameter optimization

explainable natural language inference

question answering

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

We propose an explainable inference approach for science questions by reasoning on grounding and abstract inference chains. This paper frames question answering as a natural language abductive reasoning problem, constructing plausible explanations for each candidate answer and then selecting the candidate with the best explanation as the final answer. Our method, ExplanationLP, elicits explanations by constructing a weighted graph of relevant facts for each candidate answer and employ a linear programming formalism designed to select the optimal subgraph of explanatory facts. The graphs' weighting function is composed of a set of parameters targeting relevance, cohesion and diversity, which we fine-tune for answer selection via Bayesian Optimisation. We carry out our experiments on the WorldTree and ARC-Challenge datasets to empirically demonstrate the following contributions: (1) ExplanationLP obtains strong performance when compared to transformer-based and multi-hop approaches despite having a significantly lower number of parameters (2) We illustrate that our model is able to generate plausible explanations for answer prediction
(3) Our model demonstrates better robustness towards semantic drift when compared to transformer-based and multi-hop approaches

technical paper

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This is an incredible moment for NLP. We all routinely work with models whose capabilities would have seemed like science fiction just two decades ago, powerful organizations eagerly await our latest results, and NLP technologies are playing an increasingly large role in shaping our society. As a result, all of us in the NLP community are likely to participate in research that will contribute (to varying degrees and perhaps only indirectly) to technologies that will impact many people’s lives, with both positive and negative consequences for example, technologies that broaden accessibility, enhance creative self-expression, heighten surveillance, and create propaganda. What can we do to fulfill the social responsibility that this brings? As a (very) partial answer to this question, I will review a number of important recent developments, spanning many research groups, concerning dataset creation, model introspection, and system assessment. Taken together, these ideas can help us more reliably characterize how NLP systems will behave, and more reliably communicate this information to a wider range of potential users. In this way, they can help us meet our obligations to the people whose lives are impacted by the results of our research.

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