Search Results for author: Roee Aharoni

Found 17 papers, 8 papers with code

Q^{2}: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

no code implementations EMNLP 2021 Or Honovich, Leshem Choshen, Roee Aharoni, Ella Neeman, Idan Szpektor, Omri Abend

Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.

Abstractive Text Summarization Natural Language Inference +2

$Q^{2}$: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering

1 code implementation16 Apr 2021 Or Honovich, Leshem Choshen, Roee Aharoni, Ella Neeman, Idan Szpektor, Omri Abend

Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability.

Abstractive Text Summarization Dialogue Evaluation +3

KoBE: Knowledge-Based Machine Translation Evaluation

1 code implementation Findings of the Association for Computational Linguistics 2020 Zorik Gekhman, Roee Aharoni, Genady Beryozkin, Markus Freitag, Wolfgang Macherey

Our approach achieves the highest correlation with human judgements on 9 out of the 18 language pairs from the WMT19 benchmark for evaluation without references, which is the largest number of wins for a single evaluation method on this task.

Machine Translation Translation

Real-Time Sign Language Detection using Human Pose Estimation

no code implementations11 Aug 2020 Amit Moryossef, Ioannis Tsochantaridis, Roee Aharoni, Sarah Ebling, Srini Narayanan

We propose a lightweight real-time sign language detection model, as we identify the need for such a case in videoconferencing.

Optical Flow Estimation Pose Estimation

Unsupervised Domain Clusters in Pretrained Language Models

1 code implementation ACL 2020 Roee Aharoni, Yoav Goldberg

The notion of "in-domain data" in NLP is often over-simplistic and vague, as textual data varies in many nuanced linguistic aspects such as topic, style or level of formality.

Machine Translation Pretrained Language Models +1

Diversify Your Datasets: Analyzing Generalization via Controlled Variance in Adversarial Datasets

1 code implementation CONLL 2019 Ohad Rozen, Vered Shwartz, Roee Aharoni, Ido Dagan

Phenomenon-specific "adversarial" datasets have been recently designed to perform targeted stress-tests for particular inference types.

Filling Gender \& Number Gaps in Neural Machine Translation with Black-box Context Injection

no code implementations WS 2019 Amit Moryossef, Roee Aharoni, Yoav Goldberg

When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must {``}guess{''} this missing information, often leading to incorrect translations in the given context.

Machine Translation Translation

The Missing Ingredient in Zero-Shot Neural Machine Translation

no code implementations17 Mar 2019 Naveen Arivazhagan, Ankur Bapna, Orhan Firat, Roee Aharoni, Melvin Johnson, Wolfgang Macherey

Multilingual Neural Machine Translation (NMT) models are capable of translating between multiple source and target languages.

Machine Translation Translation

Filling Gender & Number Gaps in Neural Machine Translation with Black-box Context Injection

no code implementations8 Mar 2019 Amit Moryossef, Roee Aharoni, Yoav Goldberg

When translating from a language that does not morphologically mark information such as gender and number into a language that does, translation systems must "guess" this missing information, often leading to incorrect translations in the given context.

Machine Translation Translation

Massively Multilingual Neural Machine Translation

no code implementations NAACL 2019 Roee Aharoni, Melvin Johnson, Orhan Firat

Our experiments on a large-scale dataset with 102 languages to and from English and up to one million examples per direction also show promising results, surpassing strong bilingual baselines and encouraging future work on massively multilingual NMT.

Machine Translation Translation

Split and Rephrase: Better Evaluation and Stronger Baselines

1 code implementation ACL 2018 Roee Aharoni, Yoav Goldberg

To aid this, we present a new train-development-test data split and neural models augmented with a copy-mechanism, outperforming the best reported baseline by 8. 68 BLEU and fostering further progress on the task.

Machine Translation Split and Rephrase

Split and Rephrase: Better Evaluation and a Stronger Baseline

2 code implementations2 May 2018 Roee Aharoni, Yoav Goldberg

To aid this, we present a new train-development-test data split and neural models augmented with a copy-mechanism, outperforming the best reported baseline by 8. 68 BLEU and fostering further progress on the task.

Split and Rephrase

Towards String-to-Tree Neural Machine Translation

no code implementations ACL 2017 Roee Aharoni, Yoav Goldberg

We present a simple method to incorporate syntactic information about the target language in a neural machine translation system by translating into linearized, lexicalized constituency trees.

Machine Translation Translation

Morphological Inflection Generation with Hard Monotonic Attention

1 code implementation ACL 2017 Roee Aharoni, Yoav Goldberg

We present a neural model for morphological inflection generation which employs a hard attention mechanism, inspired by the nearly-monotonic alignment commonly found between the characters in a word and the characters in its inflection.

Hard Attention Morphological Inflection

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