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Authors: nan

Publication: TIPSTER (2018)

DOI: nan

AbstractIn this paper, we propose a new universal machine translation approach focusing on languages with a limited amount of parallel data. Our proposed approach utilizes a transfer-learning approach to share …...

22346

Cross-Domain Generalization of Neural Constituency Parsers

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DOI: nan

AbstractNeural parsers obtain state-of-the-art results on benchmark treebanks for constituency parsing—but to what degree do they generalize to other domains? We present three results about the generalization of neural parsers …...

39926

Cross-lingual Spoken Language Understanding with Regularized Representation Alignment

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Publication: TIPSTER (2020)

DOI: nan

AbstractDespite the promising results of current cross-lingual models for spoken language understanding systems, they still suffer from imperfect cross-lingual representation alignments between the source and target languages, which makes the …...

29909

Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema

Authors: nan

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AbstractThe Winograd Schema (WS) has been proposed as a test for measuring commonsense capabilities of models. Recently, pre-trained language model-based approaches have boosted performance on some WS benchmarks but the …...

30659

Ensemble Fine-tuned mBERT for Translation Quality Estimation

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AbstractQuality Estimation (QE) is an important component of the machine translation workflow as it assesses the quality of the translated output without consulting reference translations. In this paper, we discuss …...

27703

Generating Questions from Wikidata Triples

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractQuestion generation from knowledge bases (or knowledge base question generation, KBQG) is the task of generating questions from structured database information, typically in the form of triples representing facts. To …...

16538

Hitachi at SemEval-2022 Task 2: On the Effectiveness of Span-based Classification Approaches for Multilingual Idiomaticity Detection

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Publication: TIPSTER (2022)

DOI: nan

AbstractIn this paper, we describe our system for SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding. The task aims at detecting idiomaticity in an input sequence (Subtask A) and …...

25155

NER4ID at SemEval-2022 Task 2: Named Entity Recognition for Idiomaticity Detection

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25147

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DOI: nan

AbstractPrompt tuning (PT) is a promising parameter-efficient method to utilize extremely large pre-trained language models (PLMs), which can achieve comparable performance to full-parameter fine-tuning by only tuning a few soft …...

21119

UBC-DLNLP at SemEval-2023 Task 12: Impact of Transfer Learning on African Sentiment Analysis

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AbstractWe describe our contribution to the SemEVAl 2023 AfriSenti-SemEval shared task, where we tackle the task of sentiment analysis in 14 different African languages. We develop both monolingual and multilingual …...

24935