After the successful completion of NALOMA’20 (NAtural LOgic Meets MAchine Learning), NALOMA’21 seeks to continue the series and attract exciting contributions. The workshop aims to bridge the gap between ML/DL and symbolic/logic-based approaches to NLI, and it is perhaps the only workshop organized to do so. It will take place from June 14-June 18, 2021, during IWCS 2021 organized by the University of Groningen but taking place fully online due to the pandemic.
NALOMA’21 is set out to address two main issues of the NLI community. First, the approaches and systems currently used to address NLI are too one-dimensional, and no fruitful dialog between them is promoted. One strand of research focuses on training large DL models that can achieve what has been identified as “human performance”. With the world-knowledge that is encapsulated in such models and their robust nature, such approaches can deal with diverse and large data in an efficient way. However, it has been repeatedly shown that such models lack generalization power and are far from solving NLI. When presented with differently biased data or with complex inferences containing hard linguistic phenomena, they struggle to reach the baseline. Explicitly detecting and solving these weaknesses is only partly possible, e.g., through appropriate datasets, because such models act like black-boxes with low explainability. Another strand of research targets more traditional approaches to reasoning, employing some kind of logic or semantic formalism. Such approaches excel in precision, especially of complex inferences with hard linguistic phenomena, e.g., negation, quantifiers, modals, etc. However, they suffer from inadequate world-knowledge and lower robustness, making it hard for them to compete with the state-of-the-art models. Overall, current methods to NLI are too one-dimensional: they are either purely DL or purely symbolic but do not attempt to combine the two worlds.
A second issue concerns datasets. Existing NLI datasets are either complex enough but too small to be used for proper learning, e.g., the FraCas or the RTE datasets, or large enough but too easy to be claimed to represent human inference, e.g.,\ SICK, SNLI, MNLI, etc. Especially the larger datasets additionally suffer from artifacts and inconsistent or misleading annotations. There have been efforts to correct some of these mistakes, but often such efforts lead to different versions of the corpora, raising comparability issues. Even more interesting is the fact that such inconsistencies often derive from the nature of the NLI task itself, which is prone to inherent disagreements, reflecting the inherent variability of the human reasoning process. Thus, there is a need for a refinement of the NLI task, the establishment of some common notions and the creation of suitable corpora, not only including more diverse data and reliable annotations, but also accounting for the inherent variability of the task. Last but not least, the datasets are mainly in English, and are therefore likely to be missing a lot of linguistically interesting phenomena.
The NALOMA workshop addresses both of these issues: the one-dimensionality of the existing approaches and the dataset weaknesses. It aims to bridge the gap between ML/DL and symbolic/logic-based approaches and it contributes to current efforts to provide data which is more reliable, more representative-of-human-inference and more linguistically diverse. NALOMA seeks to raise awareness on the data-related issues to NLI and discuss appropriate solutions. It is especially suitable for researchers interested in evaluating existing corpora and proposing new ones. The workshop wishes to place a special focus on the refinement of the NLI task and on ways to address its inherent variability.