An important goal of the MaCoCu project is to improve EU-specific NLP systems that concern their Digital Service Infrastructures (DSIs).
no code implementations • • Marta Bañón, Miquel Esplà-Gomis, Mikel L. Forcada, Cristian García-Romero, Taja Kuzman, Nikola Ljubešić, Rik van Noord, Leopoldo Pla Sempere, Gema Ramírez-Sánchez, Peter Rupnik, Vít Suchomel, Antonio Toral, Tobias van der Werff, Jaume Zaragoza
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.
We address the task of automatically distinguishing between human-translated (HT) and machine translated (MT) texts.
We present an end-to-end neural approach to generate English sentences from formal meaning representations, Discourse Representation Structures (DRSs).
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.
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.
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
Analogies such as man is to king as woman is to X are often used to illustrate the amazing power of word embeddings.
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.
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
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
A pilot study is performed to automatically find changes in meaning by comparing meaning representations of translations.
We evaluate the character-level translation method for neural semantic parsing on a large corpus of sentences annotated with Abstract Meaning Representations (AMRs).
Ranked #22 on AMR Parsing on LDC2017T10
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.
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).