SemEval-2017 Task 9: Abstract Meaning Representation Parsing and Generation

نویسندگان

  • Jonathan May
  • Jay Priyadarshi
چکیده

In this report we summarize the results of the 2017 AMR SemEval shared task. The task consisted of two separate yet related subtasks. In the parsing subtask, participants were asked to produce Abstract Meaning Representation (AMR) (Banarescu et al., 2013) graphs for a set of English sentences in the biomedical domain. In the generation subtask, participants were asked to generate English sentences given AMR graphs in the news/forum domain. A total of five sites participated in the parsing subtask, and four participated in the generation subtask. Along with a description of the task and the participants’ systems, we show various score ablations and some sample outputs.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Sheffield at SemEval-2017 Task 9: Transition-based language generation from AMR

This paper describes the submission by the University of Sheffield to the SemEval 2017 Abstract Meaning Representation Parsing and Generation task (SemEval 2017 Task 9, Subtask 2). We cast language generation from AMR as a sequence of actions (e.g., insert/remove/rename edges and nodes) that progressively transform the AMR graph into a dependency parse tree. This transition-based approach relie...

متن کامل

CLIP$@$UMD at SemEval-2016 Task 8: Parser for Abstract Meaning Representation using Learning to Search

In this paper we describe our approach to the Abstract Meaning Representation (AMR) parsing shared task as part of SemEval 2016. We develop a novel technique to parse English sentences into AMR using Learning to Search. We decompose the AMR parsing task into three subtasks that of predicting the concepts, the relations, and the root. Each of these subtasks are treated as a sequence of predictio...

متن کامل

M2L at SemEval-2016 Task 8: AMR Parsing with Neural Networks

This paper describes our contribution to the SemEval 2016 Workshop. We participated in the Shared Task 8 on Meaning Representation parsing using a transition-based approach, which builds upon the system of Wang et al. (2015a) and Wang et al. (2015b), with additions that utilize a Feedforward Neural Network classifier and an enriched feature set. We observed that exploiting Neural Networks in Ab...

متن کامل

The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer

We participated in the shared task on meaning representation parsing (Task 8 at SemEval2016) with the aim of investigating whether we could use Boxer, an existing open-domain semantic parser, for this task. However, the meaning representations produced by Boxer, Discourse Representation Structures, are considerably different from Abstract Meaning Representations, AMRs, the target meaning repres...

متن کامل

ICL-HD at SemEval-2016 Task 8: Meaning Representation Parsing - Augmenting AMR Parsing with a Preposition Semantic Role Labeling Neural Network

We describe our submission system to the SemEval-2016 Task 8 on Abstract Meaning Representation (AMR) Parsing. We attempt to improve AMR parsing by exploiting preposition semantic role labeling information retrieved from a multi-layer feed-forward neural network. Prepositional semantics is included as features into the transition-based AMR parsing system CAMR (Wang, Xue, and S. Pradhan 2015a). ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017