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Found 14 papers, 5 papers with code
Elvis vs. M. Jackson: Who has More Albums? Classification and Identification of Elements in Comparative Questions
Comparative Question Answering (cQA) is the task of providing concrete and accurate responses to queries such as: “Is Lyft cheaper than a regular taxi?” or “What makes a mortgage different from a regular loan?”.
Probing Pre-trained Language Models for Semantic Attributes and their Values
Pretrained language models (PTLMs) yield state-of-the-art performance on many natural language processing tasks, including syntax, semantics and commonsense.
SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages
We present the first shared task on Semantic Textual Relatedness (STR).
SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 14 Languages
Exploring and quantifying semantic relatedness is central to representing language.
SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)
We present the first Africentric SemEval Shared task, Sentiment Analysis for African Languages (AfriSenti-SemEval) - The dataset is available at https://github. com/afrisenti-semeval/afrisent-semeval-2023.
AfriSenti: A Twitter Sentiment Analysis Benchmark for African Languages
These include 75 languages with at least one million speakers each.
WikiBank: Using Wikidata to Improve Multilingual Frame-Semantic Parsing
Frame-semantic annotations exist for a tiny fraction of the world{'}s languages, Wikidata, however, links knowledge base triples to texts in many languages, providing a common, distant supervision signal for semantic parsers.
Naive Regularizers for Low-Resource Neural Machine Translation
Neural machine translation models have little inductive bias, which can be a disadvantage in low-resource scenarios.
Improving word alignment for low resource languages using English monolingual SRL
In contrast, our proposed model not only improve translation by injecting a monolingual objective function to learn bilingual correlations during early training of the translation model, but also helps to learn more meaningful correlations with a relatively small data set, leading to a better alignment compared to either conventional ITG or traditional GIZA++ based approaches.
