Frame- and Entity-Based Knowledge for Common-Sense Argumentative Reasoning

By Teresa Botschen, Daniil Sorokin, Iryna Gurevych. 2018

28 citations recorded by the source corpus.

Common-sense argumentative reasoning is a challenging task that requires holistic understanding of the argumentation where external knowledge about the world is hypothesized to play a key role. We explore the idea of using event knowledge about prototypical situations from FrameNet and fact knowledge about concrete entities from Wikidata to solve the task. We find that both resources can contribute to an improvement over the non-enriched approach and point out two persisting challenges: first, integration of many annotations of the same type, and second, fusion of complementary annotations. After our explorations, we question the key role of external world knowledge with respect to the argumentative reasoning task and rather point towards a logic-based analysis of the chain of reasoning.

Subfields: Artificial Intelligence, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Timothy Niven
2. Hung-Yu kao
3. Samuel R. Bowman
4. Zhiyong Lu
5. Yejin Choi

In Natural Language Processing

1. Timothy Niven
2. Hung-Yu kao
3. Samuel R. Bowman
4. Yejin Choi

Citations in the corpus

• Probing Neural Network Comprehension of Natural Language Arguments (2019, methodology): “The baselines are a bag of vectors (BoV), bidirectional LSTM (Hochreiter and Schmidhu-ber, 1997) (BiLSTM), the SemEval winner GIST (Choi and Lee, 2018), the best model of Botschen et al. (2018), and human performance (Table 1).”