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Found 10 papers, 4 papers with code
Handling Ontology Gaps in Semantic Parsing
We then present a novel hallucination detection strategy that exploits the computational graph of the NSP model to detect the NSP hallucinations in the presence of ontology gaps, out-of-domain utterances, and to recognize NSP errors, improving the F1-Score respectively by ~21, ~24% and ~1%.
MASSIVE Multilingual Abstract Meaning Representation: A Dataset and Baselines for Hallucination Detection
Abstract Meaning Representation (AMR) is a semantic formalism that captures the core meaning of an utterance.
Enhancing Contextual Understanding in Large Language Models through Contrastive Decoding
Large language models (LLMs) tend to inadequately integrate input context during text generation, relying excessively on encoded prior knowledge in model parameters, potentially resulting in generated text with factual inconsistencies or contextually unfaithful content.
We Need to Talk About Classification Evaluation Metrics in NLP
In Natural Language Processing (NLP) classification tasks such as topic categorisation and sentiment analysis, model generalizability is generally measured with standard metrics such as Accuracy, F-Measure, or AUC-ROC.
Semantic Parsing for Conversational Question Answering over Knowledge Graphs
In this paper, we are interested in developing semantic parsers which understand natural language questions embedded in a conversation with a user and ground them to formal queries over definitions in a general purpose knowledge graph (KG) with very large vocabularies (covering thousands of concept names and relations, and millions of entities).
In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering
Visual Question Answering (VQA) methods aim at leveraging visual input to answer questions that may require complex reasoning over entities.
One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets
The lack of a single standard for meaning representations led to the creation of a plethora of semantic parsing datasets.
Multilingual Neural Semantic Parsing for Low-Resourced Languages
To evaluate our multilingual models on human-written sentences as opposed to machine translated ones, we introduce a new multilingual semantic parsing dataset in English, Italian and Japanese based on the Facebook Task Oriented Parsing (TOP) dataset.
Don't Parse, Generate! A Sequence to Sequence Architecture for Task-Oriented Semantic Parsing
Virtual assistants such as Amazon Alexa, Apple Siri, and Google Assistant often rely on a semantic parsing component to understand which action(s) to execute for an utterance spoken by its users.
Transfer Learning for Neural Semantic Parsing
The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL).
