Where our study diverges from previous work is in (1) providing a more thorough analysis of what makes mathematical term extraction a difficult problem to begin with; (2) paying close attention to inter-annotator disagreements; (3) providing a set of guidelines which both human and machine annotators could use to standardize the extraction process; (4) introducing a new annotation tool to help humans with ATE, applicable to any mathematical field and even beyond mathematics; (5) using prompts to ChatGPT as part of the extraction process, and proposing best practices for such prompts; and (6) raising the question of whether ChatGPT could be used as an annotator on the same level as human experts.
Deep learning (DL) based language models achieve high performance on various benchmarks for Natural Language Inference (NLI).
Ranked #1 on Natural Language Inference on SICK
In this paper, we present the first large-scale NLI dataset (consisting of ~56, 000 annotated sentence pairs) for Chinese called the Original Chinese Natural Language Inference dataset (OCNLI).
We present a new logic-based inference engine for natural language inference (NLI) called MonaLog, which is based on natural logic and the monotonicity calculus.
Our experiments, using a library of 8 such semantic fragments, reveal two remarkable findings: (a) State-of-the-art models, including BERT, that are pre-trained on existing NLI benchmark datasets perform poorly on these new fragments, even though the phenomena probed here are central to the NLI task.
This is the proceedings of the Seventeenth conference on Theoretical Aspects of Rationality and Knowledge, 17-19 July 2019, Institut de Recherche en Informatique de Toulouse (IRIT), Toulouse University Toulouse, France.
Computer Science and Game Theory Logic in Computer Science
This paper explores relational syllogistic logics, a family of logical systems related to reasoning about relations in extensions of the classical syllogistic.