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Found 26 papers, 12 papers with code
The Parallel Meaning Bank: Towards a Multilingual Corpus of Translations Annotated with Compositional Meaning Representations
The Parallel Meaning Bank is a corpus of translations annotated with shared, formal meaning representations comprising over 11 million words divided over four languages (English, German, Italian, and Dutch).
The Meaning Factory at SemEval-2017 Task 9: Producing AMRs with Neural Semantic Parsing
We evaluate a semantic parser based on a character-based sequence-to-sequence model in the context of the SemEval-2017 shared task on semantic parsing for AMRs.
Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
We evaluate the character-level translation method for neural semantic parsing on a large corpus of sentences annotated with Abstract Meaning Representations (AMRs).
Ranked #26 on
AMR Parsing
on LDC2017T10
Evaluating Scoped Meaning Representations
A pilot study is performed to automatically find changes in meaning by comparing meaning representations of translations.
UG18 at SemEval-2018 Task 1: Generating Additional Training Data for Predicting Emotion Intensity in Spanish
The present study describes our submission to SemEval 2018 Task 1: Affect in Tweets.
Exploring Neural Methods for Parsing Discourse Representation Structures
Neural methods have had several recent successes in semantic parsing, though they have yet to face the challenge of producing meaning representations based on formal semantics.
Ranked #3 on
DRS Parsing
on PMB-3.0.0
Neural Boxer at the IWCS Shared Task on DRS Parsing
This paper describes our participation in the shared task of Discourse Representation Structure parsing.
Linguistic Information in Neural Semantic Parsing with Multiple Encoders
Recently, sequence-to-sequence models have achieved impressive performance on a number of semantic parsing tasks.
Ranked #2 on
DRS Parsing
on PMB-3.0.0
Fair is Better than Sensational:Man is to Doctor as Woman is to Doctor
However, beside the intrinsic problems with the analogy task as a bias detection tool, in this paper we show that a series of issues related to how analogies have been implemented and used might have yielded a distorted picture of bias in word embeddings.
The First Shared Task on Discourse Representation Structure Parsing
To measure similarity between two DRSs, they are represented in a clausal form, i. e. as a set of tuples.
Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor
Analogies such as man is to king as woman is to X are often used to illustrate the amazing power of word embeddings.
Character-level Representations Improve DRS-based Semantic Parsing Even in the Age of BERT
We combine character-level and contextual language model representations to improve performance on Discourse Representation Structure parsing.
Ranked #1 on
DRS Parsing
on PMB-2.2.0
The Parallel Meaning Bank: A Framework for Semantically Annotating Multiple Languages
This paper gives a general description of the ideas behind the Parallel Meaning Bank, a framework with the aim to provide an easy way to annotate compositional semantics for texts written in languages other than English.
Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese
Even with DRSs based on English, good results for Chinese are obtained.
Automatic Discrimination of Human and Neural Machine Translation in Multilingual Scenarios
We tackle the task of automatically discriminating between human and machine translations.
Controlling Topic-Focus Articulation in Meaning-to-Text Generation using Graph Neural Networks
The idea is to add pragmatic information such as topic to the meaning representation, thereby forcing either active or passive voice when given to a natural language generation system.
Do Language Models Care About Text Quality? Evaluating Web-Crawled Corpora Across 11 Languages
Large, curated, web-crawled corpora play a vital role in training language models (LMs).
Language Models on a Diet: Cost-Efficient Development of Encoders for Closely-Related Languages via Additional Pretraining
The world of language models is going through turbulent times, better and ever larger models are coming out at an unprecedented speed.
Gaining More Insight into Neural Semantic Parsing with Challenging Benchmarks
The Parallel Meaning Bank (PMB) serves as a corpus for semantic processing with a focus on semantic parsing and text generation.
Transparent Semantic Parsing with Universal Dependencies Using Graph Transformations
Even though many recent semantic parsers are based on deep learning methods, we should not forget that rule-based alternatives might offer advantages over neural approaches with respect to transparency, portability, and explainability.
MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages
We introduce the project “MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages”, funded by the Connecting Europe Facility, which is aimed at building monolingual and parallel corpora for under-resourced European languages.
Automatic Discrimination of Human and Neural Machine Translation: A Study with Multiple Pre-Trained Models and Longer Context
We address the task of automatically distinguishing between human-translated (HT) and machine translated (MT) texts.
Evaluating Text Generation from Discourse Representation Structures
We present an end-to-end neural approach to generate English sentences from formal meaning representations, Discourse Representation Structures (DRSs).
Building Domain-specific Corpora from the Web: the Case of European Digital Service Infrastructures
An important goal of the MaCoCu project is to improve EU-specific NLP systems that concern their Digital Service Infrastructures (DSIs).
