Семантические Ролевые Метки

otherentity ID: semanticrolelabels

Related Papers

CAMR at SemEval-2016 Task 8: An Extended Transition-based AMR Parser

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

This paper describes CAMR, the transition- based parser that we use in the SemEval-2016 Meaning Representation Parsing task. The main contribution of this paper is a descrip- tion of the …...

22412

Unsupervised AMR-Dependency Parse Alignment

Authors: nan

Publication: TIPSTER (2017)

DOI: nan

AbstractIn this paper, we introduce an Abstract Meaning Representation (AMR) to Dependency Parse aligner. Alignment is a preliminary step for AMR parsing, and our aligner improves current AMR parser performance. …...

43121

A Corpus for Visual Question Answering Annotated with Frame Semantic Information

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractVisual Question Answering (VQA) has been widely explored as a computer vision problem, however enhancing VQA systems with linguistic information is necessary for tackling the complexity of the task. The …...

14168

Constructing Web-Accessible Semantic Role Labels and Frames for Japanese as Additions to the NPCMJ Parsed Corpus

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractAs part of constructing the NINJAL Parsed Corpus of Modern Japanese (NPCMJ), a web-accessible language resource, we are adding frame information for predicates, together with two types of semantic role …...

14801

Aligning Images and Text with Semantic Role Labels for Fine-Grained Cross-Modal Understanding

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractAs vision processing and natural language processing continue to advance, there is increasing interest in multimodal applications, such as image retrieval, caption generation, and human-robot interaction. These tasks require close …...

14769

Evaluating Factual Consistency of Texts with Semantic Role Labeling

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractAutomated evaluation of text generation systems has recently seen increasing attention, particularly checking whether generated text stays truthful to input sources. Existing methods frequently rely on an evaluation using task-specific …...

26436