Is My Model Using The Right Evidence? Systematic Probes for Examining Evidence-Based Tabular Reasoning

While neural models routinely report state-of-the-art performance across NLP tasks involving reasoning, their outputs are often observed to not properly use and reason on the evidence presented to them in the inputs. A model that reasons properly is expected to attend to the right parts of the input, be self-consistent in its predictions across examples, avoid spurious patterns in inputs, and to ignore biasing from its underlying pre-trained language model in a nuanced, context-sensitive fashion (e.g. handling counterfactuals). Do today's models do so? In this paper, we study this question using the problem of reasoning on tabular data. The tabular nature of the input is particularly suited for the study as it admits systematic probes targeting the properties listed above. Our experiments demonstrate that a BERT-based model representative of today's state-of-the-art fails to properly reason on the following counts: it often (a) misses the relevant evidence, (b) suffers from hypothesis and knowledge biases, and, (c) relies on annotation artifacts and knowledge from pre-trained language models as primary evidence rather than relying on reasoning on the premises in the tabular input.

Shallow Parsing Pipeline for Hindi-English Code-Mixed Social Media Text

In this study, the problem of shallow parsing of Hindi-English code-mixed social media text (CSMT) has been addressed. We have annotated the data, developed a language identifier, a normalizer, a part-of-speech tagger and a shallow parser. To the best of our knowledge, we are the first to attempt shallow parsing on CSMT. The pipeline developed has been made available to the research community with the goal of enabling better text analysis of Hindi English CSMT. The pipeline is accessible at http://bit.ly/csmt-parser-api .