2020, Kalouli, Aikaterini-Lida, Sevastjanova, Rita, de Paiva, Valeria, Crouch, Richard, El-Assady, Mennatallah

Advances in Natural Language Inference (NLI) have helped us understand what state-of-the-art models really learn and what their generalization power is. Recent research has revealed some heuristics and biases of these models. However, to date, there is no systematic effort to capitalize on those insights through a system that uses these to explain the NLI decisions. To this end, we propose XplaiNLI, an eXplainable, interactive, visualization interface that computes NLI with different methods and provides explanations for the decisions made by the different approaches.

2019, Kalouli, Aikaterini-Lida, de Paiva, Valeria, Crouch, Richard

Vector representations of words have seen an increasing success over the past years in a variety of NLP tasks. While there seems to be a consensus about the usefulness of word embeddings and how to learn them, it is still unclear which representations can capture the meaning of phrases or even whole sentences. Recent work has shown that simple operations outperform more complex deep architectures. In this work, we propose two novel constraints for computing noun phrase vector representations. First, we propose that the semantic and not the syntactic contribution of each component of a noun phrase should be considered, so that the resulting composed vectors express more of the phrase meaning. Second, the composition process of the two phrase vectors should apply suitable dimensions’ selection in a way that specific semantic features captured by the phrase’s meaning become more salient. Our proposed methods are compared to 11 other approaches, including popular baselines and a neural net architecture, and are evaluated across 6 tasks and 2 datasets. Our results show that these constraints lead to more expressive phrase representations and can be applied to other state-of-the-art methods to improve their performance.

2019, Kalouli, Aikaterini-Lida, Buis, Annebeth, Real, Livy, Palmer, Martha, de Paiva, Valeria

The vast amount of research introducing new corpora and techniques for semi-automatically annotating corpora shows the important role that datasets play in today’s research, especially in the machine learning community. This rapid development raises concerns about the quality of the datasets created and consequently of the models trained, as recently discussed with respect to the Natural Language Inference (NLI) task. In this work we conduct an annotation experiment based on a small subset of the SICK corpus. The experiment reveals several problems in the annotation guidelines, and various challenges of the NLI task itself. Our quantitative evaluation of the experiment allows us to assign our empirical observations to specific linguistic phenomena and leads us to recommendations for future annotation tasks, for NLI and possibly for other tasks.

2019, Kalouli, Aikaterini-Lida, Crouch, Richard, de Paiva, Valeria

Three broad approaches have been attempted to combine distributional and structural/symbolic aspects to construct meaning representations: a) injecting linguistic features into distributional representations, b) injecting distributional features into symbolic representations or c) combining structural and distributional features in the final representation. This work focuses on an example of the third and less studied approach: it extends the Graphical Knowledge Representation (GKR) to include distributional features and proposes a division of semantic labour between the distributional and structural/symbolic features. We propose two extensions of GKR that clearly show this division and empirically test one of the proposals on an NLI dataset with hard compositional pairs.