Semantic Parsing for Conversational Question Answering over Knowledge Graphs

• URL: http://arxiv.org/abs/2301.12217v1
• Date: Sat, 28 Jan 2023 14:45:11 GMT
• Title: Semantic Parsing for Conversational Question Answering over Knowledge Graphs
• Authors: Laura Perez-Beltrachini, Parag Jain, Emilio Monti, Mirella Lapata
• Abstract summary: We develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task. Our dataset and models are released at https://github.com/Edinburgh/SPICE.
• Score: 63.939700311269156
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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). To this end, we develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task: dealing with large vocabularies, modelling conversation context, predicting queries with multiple entities, and generalising to new questions at test time. We hope our dataset will serve as useful testbed for the development of conversational semantic parsers. Our dataset and models are released at https://github.com/EdinburghNLP/SPICE.

Related papers

• AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries [56.82807063333088]
  We introduce a new benchmark, AMBROSIA, which we hope will inform and inspire the development of text-to-open programs. Our dataset contains questions showcasing three different types of ambiguity (scope ambiguity, attachment ambiguity, and vagueness) In each case, the ambiguity persists even when the database context is provided. This is achieved through a novel approach that involves controlled generation of databases from scratch.
  arXiv  Detail & Related papers  (2024-06-27T10:43:04Z)
• Integrating Large Language Models with Graph-based Reasoning for Conversational Question Answering [58.17090503446995]
  We focus on a conversational question answering task which combines the challenges of understanding questions in context and reasoning over evidence gathered from heterogeneous sources like text, knowledge graphs, tables, and infoboxes. Our method utilizes a graph structured representation to aggregate information about a question and its context.
  arXiv  Detail & Related papers  (2024-06-14T13:28:03Z)
• Prompting-based Synthetic Data Generation for Few-Shot Question Answering [23.97949073816028]
  We show that using large language models can improve Question Answering performance on various datasets in the few-shot setting. We suggest that language models contain valuable task-agnostic knowledge that can be used beyond the common pre-training/fine-tuning scheme.
  arXiv  Detail & Related papers  (2024-05-15T13:36:43Z)
• Evaluating Large Language Models in Semantic Parsing for Conversational Question Answering over Knowledge Graphs [6.869834883252353]
  This paper evaluates the performance of large language models that have not been explicitly pre-trained on this task. Our results demonstrate that large language models are capable of generating graph queries from dialogues.
  arXiv  Detail & Related papers  (2024-01-03T12:28:33Z)
• Conversational Semantic Parsing using Dynamic Context Graphs [68.72121830563906]
  We consider the task of conversational semantic parsing over general purpose knowledge graphs (KGs) with millions of entities, and thousands of relation-types. We focus on models which are capable of interactively mapping user utterances into executable logical forms.
  arXiv  Detail & Related papers  (2023-05-04T16:04:41Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding [84.04706075621013]
  We present a general-purpose, modular neural semantic parsing framework based on token-level fine-grained query understanding. Our framework consists of three modules: named entity recognizer (NER), neural entity linker (NEL) and neural entity linker (NSP)
  arXiv  Detail & Related papers  (2022-09-28T21:00:30Z)
• An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs [9.979689965471428]
  This paper contributes to the state-of-the-art by extending an existing ConvQA dataset with verbalized answers. We perform experiments with five sequence-to-sequence models on generating answer responses while maintaining grammatical correctness.
  arXiv  Detail & Related papers  (2022-08-13T21:21:28Z)
• Multilingual Compositional Wikidata Questions [9.602430657819564]
  We propose a method for creating a multilingual, parallel dataset of question-Query pairs grounded in Wikidata. We use this data to train semantics for Hebrew, Kannada, Chinese and English to better understand the current strengths and weaknesses of multilingual semantic parsing.
  arXiv  Detail & Related papers  (2021-08-07T19:40:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.