Ari Rappoport


2020

pdf bib
Semantic Structural Decomposition for Neural Machine Translation
Elior Sulem | Omri Abend | Ari Rappoport
Proceedings of the Ninth Joint Conference on Lexical and Computational Semantics

Building on recent advances in semantic parsing and text simplification, we investigate the use of semantic splitting of the source sentence as preprocessing for machine translation. We experiment with a Transformer model and evaluate using large-scale crowd-sourcing experiments. Results show a significant increase in fluency on long sentences on an English-to- French setting with a training corpus of 5M sentence pairs, while retaining comparable adequacy. We also perform a manual analysis which explores the tradeoff between adequacy and fluency in the case where all sentence lengths are considered.

2019

pdf bib
Preparing SNACS for Subjects and Objects
Adi Shalev | Jena D. Hwang | Nathan Schneider | Vivek Srikumar | Omri Abend | Ari Rappoport
Proceedings of the First International Workshop on Designing Meaning Representations

Research on adpositions and possessives in multiple languages has led to a small inventory of general-purpose meaning classes that disambiguate tokens. Importantly, that work has argued for a principled separation of the semantic role in a scene from the function coded by morphosyntax. Here, we ask whether this approach can be generalized beyond adpositions and possessives to cover all scene participants—including subjects and objects—directly, without reference to a frame lexicon. We present new guidelines for English and the results of an interannotator agreement study.

pdf bib
Content Differences in Syntactic and Semantic Representation
Daniel Hershcovich | Omri Abend | Ari Rappoport
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Syntactic analysis plays an important role in semantic parsing, but the nature of this role remains a topic of ongoing debate. The debate has been constrained by the scarcity of empirical comparative studies between syntactic and semantic schemes, which hinders the development of parsing methods informed by the details of target schemes and constructions. We target this gap, and take Universal Dependencies (UD) and UCCA as a test case. After abstracting away from differences of convention or formalism, we find that most content divergences can be ascribed to: (1) UCCA’s distinction between a Scene and a non-Scene; (2) UCCA’s distinction between primary relations, secondary ones and participants; (3) different treatment of multi-word expressions, and (4) different treatment of inter-clause linkage. We further discuss the long tail of cases where the two schemes take markedly different approaches. Finally, we show that the proposed comparison methodology can be used for fine-grained evaluation of UCCA parsing, highlighting both challenges and potential sources for improvement. The substantial differences between the schemes suggest that semantic parsers are likely to benefit downstream text understanding applications beyond their syntactic counterparts.

pdf bib
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA
Daniel Hershcovich | Zohar Aizenbud | Leshem Choshen | Elior Sulem | Ari Rappoport | Omri Abend
Proceedings of the 13th International Workshop on Semantic Evaluation

We present the SemEval 2019 shared task on Universal Conceptual Cognitive Annotation (UCCA) parsing in English, German and French, and discuss the participating systems and results. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. The shared task has yielded improvements over the state-of-the-art baseline in all languages and settings. Full results can be found in the task’s website https://competitions.codalab.org/competitions/19160.

2018

pdf bib
Universal Dependency Parsing with a General Transition-Based DAG Parser
Daniel Hershcovich | Omri Abend | Ari Rappoport
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

This paper presents our experiments with applying TUPA to the CoNLL 2018 UD shared task. TUPA is a general neural transition-based DAG parser, which we use to present the first experiments on recovering enhanced dependencies as part of the general parsing task. TUPA was designed for parsing UCCA, a cross-linguistic semantic annotation scheme, exhibiting reentrancy, discontinuity and non-terminal nodes. By converting UD trees and graphs to a UCCA-like DAG format, we train TUPA almost without modification on the UD parsing task. The generic nature of our approach lends itself naturally to multitask learning.

pdf bib
Semantic Structural Evaluation for Text Simplification
Elior Sulem | Omri Abend | Ari Rappoport
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Current measures for evaluating text simplification systems focus on evaluating lexical text aspects, neglecting its structural aspects. In this paper we propose the first measure to address structural aspects of text simplification, called SAMSA. It leverages recent advances in semantic parsing to assess simplification quality by decomposing the input based on its semantic structure and comparing it to the output. SAMSA provides a reference-less automatic evaluation procedure, avoiding the problems that reference-based methods face due to the vast space of valid simplifications for a given sentence. Our human evaluation experiments show both SAMSA’s substantial correlation with human judgments, as well as the deficiency of existing reference-based measures in evaluating structural simplification.

pdf bib
BLEU is Not Suitable for the Evaluation of Text Simplification
Elior Sulem | Omri Abend | Ari Rappoport
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

BLEU is widely considered to be an informative metric for text-to-text generation, including Text Simplification (TS). TS includes both lexical and structural aspects. In this paper we show that BLEU is not suitable for the evaluation of sentence splitting, the major structural simplification operation. We manually compiled a sentence splitting gold standard corpus containing multiple structural paraphrases, and performed a correlation analysis with human judgments. We find low or no correlation between BLEU and the grammaticality and meaning preservation parameters where sentence splitting is involved. Moreover, BLEU often negatively correlates with simplicity, essentially penalizing simpler sentences.

pdf bib
Simple and Effective Text Simplification Using Semantic and Neural Methods
Elior Sulem | Omri Abend | Ari Rappoport
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Sentence splitting is a major simplification operator. Here we present a simple and efficient splitting algorithm based on an automatic semantic parser. After splitting, the text is amenable for further fine-tuned simplification operations. In particular, we show that neural Machine Translation can be effectively used in this situation. Previous application of Machine Translation for simplification suffers from a considerable disadvantage in that they are over-conservative, often failing to modify the source in any way. Splitting based on semantic parsing, as proposed here, alleviates this issue. Extensive automatic and human evaluation shows that the proposed method compares favorably to the state-of-the-art in combined lexical and structural simplification.

pdf bib
Multitask Parsing Across Semantic Representations
Daniel Hershcovich | Omri Abend | Ari Rappoport
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The ability to consolidate information of different types is at the core of intelligence, and has tremendous practical value in allowing learning for one task to benefit from generalizations learned for others. In this paper we tackle the challenging task of improving semantic parsing performance, taking UCCA parsing as a test case, and AMR, SDP and Universal Dependencies (UD) parsing as auxiliary tasks. We experiment on three languages, using a uniform transition-based system and learning architecture for all parsing tasks. Despite notable conceptual, formal and domain differences, we show that multitask learning significantly improves UCCA parsing in both in-domain and out-of-domain settings.

2017

pdf bib
The State of the Art in Semantic Representation
Omri Abend | Ari Rappoport
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Semantic representation is receiving growing attention in NLP in the past few years, and many proposals for semantic schemes (e.g., AMR, UCCA, GMB, UDS) have been put forth. Yet, little has been done to assess the achievements and the shortcomings of these new contenders, compare them with syntactic schemes, and clarify the general goals of research on semantic representation. We address these gaps by critically surveying the state of the art in the field.

pdf bib
A Transition-Based Directed Acyclic Graph Parser for UCCA
Daniel Hershcovich | Omri Abend | Ari Rappoport
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We present the first parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. To our knowledge, the conjunction of these formal properties is not supported by any existing parser. Our transition-based parser, which uses a novel transition set and features based on bidirectional LSTMs, has value not just for UCCA parsing: its ability to handle more general graph structures can inform the development of parsers for other semantic DAG structures, and in languages that frequently use discontinuous structures.

pdf bib
UCCAApp: Web-application for Syntactic and Semantic Phrase-based Annotation
Omri Abend | Shai Yerushalmi | Ari Rappoport
Proceedings of ACL 2017, System Demonstrations

pdf bib
Automatic Selection of Context Configurations for Improved Class-Specific Word Representations
Ivan Vulić | Roy Schwartz | Ari Rappoport | Roi Reichart | Anna Korhonen
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)

This paper is concerned with identifying contexts useful for training word representation models for different word classes such as adjectives (A), verbs (V), and nouns (N). We introduce a simple yet effective framework for an automatic selection of class-specific context configurations. We construct a context configuration space based on universal dependency relations between words, and efficiently search this space with an adapted beam search algorithm. In word similarity tasks for each word class, we show that our framework is both effective and efficient. Particularly, it improves the Spearman’s rho correlation with human scores on SimLex-999 over the best previously proposed class-specific contexts by 6 (A), 6 (V) and 5 (N) rho points. With our selected context configurations, we train on only 14% (A), 26.2% (V), and 33.6% (N) of all dependency-based contexts, resulting in a reduced training time. Our results generalise: we show that the configurations our algorithm learns for one English training setup outperform previously proposed context types in another training setup for English. Moreover, basing the configuration space on universal dependencies, it is possible to transfer the learned configurations to German and Italian. We also demonstrate improved per-class results over other context types in these two languages..

2016

pdf bib
Symmetric Patterns and Coordinations: Fast and Enhanced Representations of Verbs and Adjectives
Roy Schwartz | Roi Reichart | Ari Rappoport
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

pdf bib
Edge-Linear First-Order Dependency Parsing with Undirected Minimum Spanning Tree Inference
Effi Levi | Roi Reichart | Ari Rappoport
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2015

pdf bib
Symmetric Pattern Based Word Embeddings for Improved Word Similarity Prediction
Roy Schwartz | Roi Reichart | Ari Rappoport
Proceedings of the Nineteenth Conference on Computational Natural Language Learning

pdf bib
Conceptual Annotations Preserve Structure Across Translations: A French-English Case Study
Elior Sulem | Omri Abend | Ari Rappoport
Proceedings of the 1st Workshop on Semantics-Driven Statistical Machine Translation (S2MT 2015)

pdf bib
How Well Do Distributional Models Capture Different Types of Semantic Knowledge?
Dana Rubinstein | Effi Levi | Roy Schwartz | Ari Rappoport
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2014

pdf bib
Minimally Supervised Classification to Semantic Categories using Automatically Acquired Symmetric Patterns
Roy Schwartz | Roi Reichart | Ari Rappoport
Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers

2013

pdf bib
Authorship Attribution of Micro-Messages
Roy Schwartz | Oren Tsur | Ari Rappoport | Moshe Koppel
Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing

pdf bib
UCCA: A Semantics-based Grammatical Annotation Scheme
Omri Abend | Ari Rappoport
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers

pdf bib
Universal Conceptual Cognitive Annotation (UCCA)
Omri Abend | Ari Rappoport
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2012

pdf bib
Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP
Omri Abend | Chris Biemann | Anna Korhonen | Ari Rappoport | Roi Reichart | Anders Søgaard
Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP

pdf bib
A Diverse Dirichlet Process Ensemble for Unsupervised Induction of Syntactic Categories
Roi Reichart | Gal Elidan | Ari Rappoport
Proceedings of COLING 2012

pdf bib
Learnability-Based Syntactic Annotation Design
Roy Schwartz | Omri Abend | Ari Rappoport
Proceedings of COLING 2012

2011

pdf bib
Proceedings of the First workshop on Unsupervised Learning in NLP
Omri Abend | Anna Korhonen | Ari Rappoport | Roi Reichart
Proceedings of the First workshop on Unsupervised Learning in NLP

pdf bib
Neutralizing Linguistically Problematic Annotations in Unsupervised Dependency Parsing Evaluation
Roy Schwartz | Omri Abend | Roi Reichart | Ari Rappoport
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies

2010

pdf bib
Improved Unsupervised POS Induction Using Intrinsic Clustering Quality and a Zipfian Constraint
Roi Reichart | Raanan Fattal | Ari Rappoport
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

pdf bib
Type Level Clustering Evaluation: New Measures and a POS Induction Case Study
Roi Reichart | Omri Abend | Ari Rappoport
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

pdf bib
Semi-Supervised Recognition of Sarcasm in Twitter and Amazon
Dmitry Davidov | Oren Tsur | Ari Rappoport
Proceedings of the Fourteenth Conference on Computational Natural Language Learning

pdf bib
Automated Translation of Semantic Relationships
Dmitry Davidov | Ari Rappoport
Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010)

pdf bib
Enhanced Sentiment Learning Using Twitter Hashtags and Smileys
Dmitry Davidov | Oren Tsur | Ari Rappoport
Coling 2010: Posters

pdf bib
A Multi-Domain Web-Based Algorithm for POS Tagging of Unknown Words
Shulamit Umansky-Pesin | Roi Reichart | Ari Rappoport
Coling 2010: Posters

pdf bib
Bilingual Lexicon Generation Using Non-Aligned Signatures
Daphna Shezaf | Ari Rappoport
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

pdf bib
Fully Unsupervised Core-Adjunct Argument Classification
Omri Abend | Ari Rappoport
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

pdf bib
Improved Unsupervised POS Induction through Prototype Discovery
Omri Abend | Roi Reichart | Ari Rappoport
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

pdf bib
Extraction and Approximation of Numerical Attributes from the Web
Dmitry Davidov | Ari Rappoport
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics

pdf bib
Tense Sense Disambiguation: A New Syntactic Polysemy Task
Roi Reichart | Ari Rappoport
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

pdf bib
Improved Fully Unsupervised Parsing with Zoomed Learning
Roi Reichart | Ari Rappoport
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing

2009

pdf bib
Geo-mining: Discovery of Road and Transport Networks Using Directional Patterns
Dmitry Davidov | Ari Rappoport
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

pdf bib
Multi-Word Expression Identification Using Sentence Surface Features
Ram Boukobza | Ari Rappoport
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

pdf bib
Enhancement of Lexical Concepts Using Cross-lingual Web Mining
Dmitry Davidov | Ari Rappoport
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

pdf bib
Unsupervised Concept Discovery In Hebrew Using Simple Unsupervised Word Prefix Segmentation for Hebrew and Arabic
Elad Dinur | Dmitry Davidov | Ari Rappoport
Proceedings of the EACL 2009 Workshop on Computational Approaches to Semitic Languages

pdf bib
Sample Selection for Statistical Parsers: Cognitively Driven Algorithms and Evaluation Measures
Roi Reichart | Ari Rappoport
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

pdf bib
Superior and Efficient Fully Unsupervised Pattern-based Concept Acquisition Using an Unsupervised Parser
Dmitry Davidov | Roi Reichart | Ari Rappoport
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

pdf bib
Automatic Selection of High Quality Parses Created By a Fully Unsupervised Parser
Roi Reichart | Ari Rappoport
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

pdf bib
The NVI Clustering Evaluation Measure
Roi Reichart | Ari Rappoport
Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009)

pdf bib
Unsupervised Argument Identification for Semantic Role Labeling
Omri Abend | Roi Reichart | Ari Rappoport
Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP

pdf bib
Translation and Extension of Concepts Across Languages
Dmitry Davidov | Ari Rappoport
Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)

2008

pdf bib
Classification of Semantic Relationships between Nominals Using Pattern Clusters
Dmitry Davidov | Ari Rappoport
Proceedings of ACL-08: HLT

pdf bib
Unsupervised Discovery of Generic Relationships Using Pattern Clusters and its Evaluation by Automatically Generated SAT Analogy Questions
Dmitry Davidov | Ari Rappoport
Proceedings of ACL-08: HLT

pdf bib
Multi-Task Active Learning for Linguistic Annotations
Roi Reichart | Katrin Tomanek | Udo Hahn | Ari Rappoport
Proceedings of ACL-08: HLT

pdf bib
Extraction of Entailed Semantic Relations Through Syntax-Based Comma Resolution
Vivek Srikumar | Roi Reichart | Mark Sammons | Ari Rappoport | Dan Roth
Proceedings of ACL-08: HLT

pdf bib
A Supervised Algorithm for Verb Disambiguation into VerbNet Classes
Omri Abend | Roi Reichart | Ari Rappoport
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

pdf bib
Unsupervised Induction of Labeled Parse Trees by Clustering with Syntactic Features
Roi Reichart | Ari Rappoport
Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008)

2007

pdf bib
Using Classifier Features for Studying the Effect of Native Language on the Choice of Written Second Language Words
Oren Tsur | Ari Rappoport
Proceedings of the Workshop on Cognitive Aspects of Computational Language Acquisition

pdf bib
Fully Unsupervised Discovery of Concept-Specific Relationships by Web Mining
Dmitry Davidov | Ari Rappoport | Moshe Koppel
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

pdf bib
An Ensemble Method for Selection of High Quality Parses
Roi Reichart | Ari Rappoport
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

pdf bib
Self-Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets
Roi Reichart | Ari Rappoport
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics

2006

pdf bib
Efficient Unsupervised Discovery of Word Categories Using Symmetric Patterns and High Frequency Words
Dmitry Davidov | Ari Rappoport
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics

pdf bib
Induction of cross-language affix and letter sequence correspondence
Ari Rappoport | Tsahi Levent-Levi
Proceedings of the Cross-Language Knowledge Induction Workshop

2005

pdf bib
A Second Language Acquisition Model Using Example Generalization and Concept Categories
Ari Rappoport | Vera Sheinman
Proceedings of the Workshop on Psychocomputational Models of Human Language Acquisition