GIST at SemEval-2018 Task 12: A network transferring inference knowledge to Argument Reasoning Comprehension task

By Hongseok Choi, Hyunju Lee. 2018

18 citations recorded by the source corpus.

This paper describes our GIST team system that participated in SemEval-2018 Argument Reasoning Comprehension task (Task 12). Here, we address two challenging factors: unstated common senses and two lexically close warrants that lead to contradicting claims. A key idea for our system is full use of transfer learning from the Natural Language Inference (NLI) task to this task. We used Enhanced Sequential Inference Model (ESIM) to learn the NLI dataset. We describe how to use ESIM for transfer learning to choose correct warrant through a proposed system. We show comparable results through ablation experiments. Our system ranked 1st among 22 systems, outperforming all the systems more than 10%.

Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

Probing Neural Network Comprehension of Natural Language Arguments (2019, methodology): “The most successful previous work on ARCT (Choi and Lee, 2018; Zhao et al., 2018; Niven and Kao, 2018) involved transfer learning from Natural Language Inference (NLI) datasets (Bowman et al., 2015; Williams et al., 2017), and utilized effective NLI models such as ESIM (Chen et al., 2016) and…”
Frame- and Entity-Based Knowledge for Common-Sense Argumentative Reasoning (2018, background): “The shared task winner, GIST (Choi and Lee, 2018), transfers inference knowledge (SNLI, Bowman et al., 2015) to the task of ARC and beneﬁts from similar information in both datasets.”