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Found 15 papers, 6 papers with code
Named Graphs for Semantic Representation
A position paper arguing that purely graphical representations for natural language semantics lack a fundamental degree of expressiveness, and cannot deal with even basic Boolean operations like negation or disjunction.
GKR: the Graphical Knowledge Representation for semantic parsing
This paper describes the first version of an open-source semantic parser that creates graphical representations of sentences to be used for further semantic processing, e. g. for natural language inference, reasoning and semantic similarity.
ParHistVis: Visualization of Parallel Multilingual Historical Data
The study of language change through parallel corpora can be advantageous for the analysis of complex interactions between time, text domain and language.
GKR: Bridging the Gap between Symbolic/structural and Distributional Meaning Representations
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.
Explaining Simple Natural Language Inference
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.
Composing 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.
XplaiNLI: Explainable Natural Language Inference through Visual Analytics
Advances in Natural Language Inference (NLI) have helped us understand what state-of-the-art models really learn and what their generalization power is.
Hy-NLI: a Hybrid system for Natural Language Inference
Despite the advances in Natural Language Inference through the training of massive deep models, recent work has revealed the generalization difficulties of such models, which fail to perform on adversarial datasets with challenging linguistic phenomena.
Explaining Contextualization in Language Models using Visual Analytics
Despite the success of contextualized language models on various NLP tasks, it is still unclear what these models really learn.
Negation, Coordination, and Quantifiers in Contextualized Language Models
With the success of contextualized language models, much research explores what these models really learn and in which cases they still fail.
KonTra at CMCL 2021 Shared Task: Predicting Eye Movements by Combining BERT with Surface, Linguistic and Behavioral Information
This paper describes the submission of the team KonTra to the CMCL 2021 Shared Task on eye-tracking prediction.
Is that really a question? Going beyond factoid questions in NLP
Research in NLP has mainly focused on factoid questions, with the goal of finding quick and reliable ways of matching a query to an answer.
