1 code implementation • EMNLP (LAW, DMR) 2021 • Shira Wein, Nathan Schneider
Translation divergences are varied and widespread, challenging approaches that rely on parallel text.
1 code implementation • LREC (LAW) 2022 • Shira Wein, Wai Ching Leung, Yifu Mu, Nathan Schneider
In this work, we investigate the similarity of AMR annotations in parallel data and how much the language matters in terms of the graph structure.
1 code implementation • COLING 2022 • Shira Wein, Nathan Schneider
Cross-lingual Abstract Meaning Representation (AMR) parsers are currently evaluated in comparison to gold English AMRs, despite parsing a language other than English, due to the lack of multilingual AMR evaluation metrics.
1 code implementation • 1 Jun 2023 • Juri Opitz, Shira Wein, Julius Steen, Anette Frank, Nathan Schneider
The task of natural language inference (NLI) asks whether a given premise (expressed in NL) entails a given NL hypothesis.
1 code implementation • 23 Apr 2023 • Shira Wein, Nathan Schneider
Though individual translated texts are often fluent and preserve meaning, at a large scale, translated texts have statistical tendencies which distinguish them from text originally written in the language ("translationese") and can affect model performance.
no code implementations • 6 Oct 2022 • Shira Wein, Zhuxin Wang, Nathan Schneider
Identifying semantically equivalent sentences is important for many cross-lingual and mono-lingual NLP tasks.
no code implementations • 15 Apr 2022 • Shira Wein, Lucia Donatelli, Ethan Ricker, Calvin Engstrom, Alex Nelson, Nathan Schneider
The Abstract Meaning Representation (AMR) formalism, designed originally for English, has been adapted to a number of languages.
1 code implementation • COLING (LAW) 2020 • Michael Kranzlein, Emma Manning, Siyao Peng, Shira Wein, Aryaman Arora, Bradford Salen, Nathan Schneider
We present the Prepositions Annotated with Supersense Tags in Reddit International English ("PASTRIE") corpus, a new dataset containing manually annotated preposition supersenses of English data from presumed speakers of four L1s: English, French, German, and Spanish.
no code implementations • COLING (LAW) 2020 • Luke Gessler, Shira Wein, Nathan Schneider
Prepositional supersense annotation is time-consuming and requires expert training.
no code implementations • WS 2020 • Shira Wein
The Spanish Learner Language Oral Corpora (SPLLOC) of transcribed conversations between investigators and language learners contains a set of neologism tags.
no code implementations • COLING 2020 • Emma Manning, Shira Wein, Nathan Schneider
Most current state-of-the art systems for generating English text from Abstract Meaning Representation (AMR) have been evaluated only using automated metrics, such as BLEU, which are known to be problematic for natural language generation.