2402 00723 Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoders

Semantics, the study of meaning, is central to research in Natural Language Processing (NLP) and many other fields connected to Artificial Intelligence. Nevertheless, how semantics is understood in NLP ranges from traditional, formal linguistic definitions based on logic and the principle of compositionality to more applied notions based on grounding meaning in real-world objects and real-time interaction. We review the state of computational semantics in NLP and investigate how different lines of inquiry reflect distinct understandings of semantics and prioritize different layers of linguistic meaning. In conclusion, we identify several important goals of the field and describe how current research addresses them. In finance, NLP can be paired with machine learning to generate financial reports based on invoices, statements and other documents.

In 15, the opposition between the Agent’s possession in e1 and non-possession in e3 of the Theme makes clear that once the Agent transfers the Theme, the Agent no longer possesses it. However, in 16, the E variable in the initial has_information predicate shows that the Agent retains knowledge of the Topic even after it is transferred to the Recipient in e2. The next stage involved developing representations for classes that primarily dealt with states and processes. Because our representations for change events necessarily included state subevents and often included process subevents, we had already developed principles for how to represent states and processes.

Introduction to Natural Language Processing (NLP)

We have bots that can write simple sports articles (Puduppully et al., 2019) and programs that will syntactically parse a sentence with very high accuracy (He and Choi, 2020). But question-answering systems still get poor results for questions that require drawing inferences from documents or interpreting figurative language. Just identifying the successive locations of an entity throughout an event described in a document is a difficult computational task. This paper introduces a semantics-aware approach to natural language inference which allows neural network models to perform better on natural language inference benchmarks.

The goal is to track the changes in states of entities within a paragraph (or larger unit of discourse). This change could be in location, internal state, or physical state of the mentioned entities. For instance, a Question Answering system could benefit from predicting that entity E has been DESTROYED or has MOVED to a new location at a certain point in the text, so it can update its state tracking model and would make correct inferences.

Common NLP tasks

During our study, this study observed that certain sentences from the original text of The Analects were absent in some English translations. To maintain consistency in the similarity calculations within the parallel corpus, this study used “None” to represent untranslated sections, ensuring nlp semantics that these omissions did not impact our computational analysis. The analysis encompassed a total of 136,171 English words and 890 lines across all five translations. Here, we showcase the finer points of how these different forms are applied across classes to convey aspectual nuance.