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Found 6 papers, 3 papers with code
Structure-aware Sentence Encoder in Bert-Based Siamese Network
Recently, impressive performance on various natural language understanding tasks has been achieved by explicitly incorporating syntax and semantic information into pre-trained models, such as BERT and RoBERTa.
Natural Language Understanding
Semantic Textual Similarity
+4
Predicate-Argument Based Bi-Encoder for Paraphrase Identification
Paraphrase identification involves identifying whether a pair of sentences express the same or similar meanings.
On Training Data Influence of GPT Models
This paper presents GPTfluence, a novel approach that leverages a featurized simulation to assess the impact of training examples on the training dynamics of GPT models.
HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-lingual Natural Language Generalization
These benchmarks have overlooked the vast landscape of massively multilingual NL to multilingual code, leaving a critical gap in the evaluation of multilingual LLMs.
Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders
Therefore, we here propose to combine sentence encoders with an alignment component by representing each sentence as a list of predicate-argument spans (where their span representations are derived from sentence encoders), and decomposing the sentence-level meaning comparison into the alignment between their spans for paraphrase identification tasks.
Representing Syntax and Composition with Geometric Transformations
The exploitation of syntactic graphs (SyGs) as a word's context has been shown to be beneficial for distributional semantic models (DSMs), both at the level of individual word representations and in deriving phrasal representations via composition.
