Daniel Hershcovich

דניאל הרשקוביץ

I am a postdoctoral researcher at the CoAStaL NLP research group at the Department of Computer Science, University of Copenhagen.

I am interested in representation of meaning in natural language, and specifically, analysis of graph-structured representations of sentence meaning (examples). I am working on extending the impact of meaning representation to applications such as question answering using knowledge bases.

I completed my Ph.D. on Natural Language Processing in the Program in Brain Research: Computation and Information Processing of the Edmond & Lily Safra Center for Brain Sciences, with Ari Rappoport and Omri Abend at the School of Computer Science and Engineering of The Hebrew University of Jerusalem.

During my Ph.D., I developed a parser for UCCA, a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work, and supports rapid annotation.

I did my B.Sc. in Mathematics and Computer Science at the Open University of Israel. During 2008-2019, I was a software engineer at IBM Research, where I was part of Project Debater.

I am co-organizing a shared task on Cross-Framework Meaning Representation Parsing (MRP 2019) in CoNLL 2019, to be held on November 3-4 in Hong Kong (collocated with EMNLP-IJCNLP 2019). The task includes SDP, EDS, AMR and UCCA parsing.

(on Google Scholar, on Semantic Scholar)

MRP 2019: Cross-Framework Meaning Representation Parsing.
Stephan Oepen, Omri Abend, Jan Hajič, Daniel Hershcovich, Marco Kuhlmann, Tim O'Gorman, Nianwen Xue, Jayeol Chun, Milan Straka and Zdeňka Urešová.
CoNLL 2019 shared task.

pdf bib website
TUPA at MRP 2019: A Multi-Task Baseline System.
Daniel Hershcovich and Ofir Arviv.
CoNLL 2019 shared task.

pdf bib poster
Rewarding Coreference Resolvers for Being Consistent with World Knowledge.
Rahul Aralikatte, Heather Lent, Ana Valeria Gonzalez, Daniel Hershcovich, Chen Qiu, Anders Sandholm, Michael Ringgaard and Anders Søgaard.
EMNLP-IJCNLP 2019 (short paper).

pdf arXiv abstract bib code

Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples. We show how to improve coreference resolvers by forwarding their input to a relation extraction system and reward the resolvers for producing triples that are found in knowledge bases. Since relation extraction systems can rely on different forms of supervision and be biased in different ways, we obtain the best performance, improving over the state of the art, using multi-task reinforcement learning.
The Language of Legal and Illegal Activity on the Darknet.
Leshem Choshen*, Dan Eldad*, Daniel Hershcovich*, Elior Sulem* and Omri Abend.
ACL 2019 (long paper).

pdf arXiv Anthology abstract bib slides code

The non-indexed parts of the Internet (the Darknet) have become a haven for both legal and illegal anonymous activity. Given the magnitude of these networks, scalably monitoring their activity necessarily relies on automated tools, and notably on NLP tools. However, little is known about what characteristics texts communicated through the Darknet have, and how well off-the-shelf NLP tools do on this domain. This paper tackles this gap and performs an in-depth investigation of the characteristics of legal and illegal text in the Darknet, comparing it to a clear net website with similar content as a control condition. Taking drug-related websites as a test case, we find that texts for selling legal and illegal drugs have several linguistic characteristics that distinguish them from one another, as well as from the control condition, among them the distribution of POS tags, and the coverage of their named entities in Wikipedia.
Argument Invention from First Principles.
Yonatan Bilu, Ariel Gera, Daniel Hershcovich, Benjamin Sznajder, Dan Lahav, Guy Moshkowich, Anael Malet, Assaf Gavron and Noam Slonim.
ACL 2019 (long paper).

pdf arXiv Anthology abstract bib

Competitive debaters often find themselves facing a challenging task -- how to debate a topic they know very little about, with only minutes to prepare, and without access to books or the Internet? What they often do is rely on ''first principles'', commonplace arguments which are relevant to many topics, and which they have refined in past debates. In this work we aim to explicitly define a taxonomy of such principled recurring arguments, and, given a controversial topic, to automatically identify which of these arguments are relevant to the topic. As far as we know, this is the first time that this approach to argument invention is formalized and made explicit in the context of NLP. The main goal of this work is to show that it is possible to define such a taxonomy. While the taxonomy suggested here should be thought of as a ''first attempt'' it is nonetheless coherent, covers well the relevant topics and coincides with what professional debaters actually argue in their speeches, and facilitates automatic argument invention for new topics.
SemEval 2019 Task 1: Cross-lingual Semantic Parsing with UCCA.
Daniel Hershcovich, Zohar Aizenbud, Leshem Choshen, Elior Sulem, Ari Rappoport and Omri Abend.
SemEval 2019 shared task.

pdf arXiv Anthology abstract bib slides website

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.
Syntactic Interchangeability in Word Embedding Models.
Daniel Hershcovich, Assaf Toledo, Alon Halfon and Noam Slonim.
RepEval 2019.

pdf arXiv Anthology abstract bib poster slide code

Nearest neighbors in word embedding models are commonly observed to be semantically similar, but the relations between them can vary greatly. We investigate the extent to which word embedding models preserve syntactic interchangeability, as reflected by distances between word vectors, and the effect of hyper-parameters---context window size in particular. We use part of speech (POS) as a proxy for syntactic interchangeability, as generally speaking, words with the same POS are syntactically valid in the same contexts. We also investigate the relationship between interchangeability and similarity as judged by commonly-used word similarity benchmarks, and correlate the result with the performance of word embedding models on these benchmarks. Our results will inform future research and applications in the selection of word embedding model, suggesting a principle for an appropriate selection of the context window size parameter depending on the use-case.
Content Differences in Syntactic and Semantic Representations.
Daniel Hershcovich, Omri Abend and Ari Rappoport.
NAACL 2019 (long paper).

pdf arXiv Anthology abstract bib supplementary material poster code data examples

Syntactic analysis plays an important role in semantic parsing, but 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.
Universal Dependency Parsing with a General Transition-Based DAG Parser.
Daniel Hershcovich, Omri Abend and Ari Rappoport.
CoNLL 2018 UD Shared Task.

pdf arXiv Anthology abstract bib poster code

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.
Multitask Parsing Across Semantic Representations.
Daniel Hershcovich, Omri Abend and Ari Rappoport.
ACL 2018 (long paper).

pdf arXiv Anthology abstract bib supplementary material poster code data

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.
A Transition-Based Directed Acyclic Graph Parser for UCCA.
Daniel Hershcovich, Omri Abend and Ari Rappoport.
ACL 2017 (long paper), Outstanding Paper Award.

pdf arXiv Anthology abstract bib supplementary material slides poster code demo data

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.
Automatic Claim Negation: Why, How and When.
Yonatan Bilu, Daniel Hershcovich, Noam Slonim.
NAACL HLT 2015 (long paper).

Anthology abstract bib

The main goal of argumentation mining is to analyze argumentative structures within an argument-rich document, and reason about their composition. Recently, there is also interest in the task of simply detecting claims (sometimes called conclusion) in general documents. In this work we ask how this set of detected claims can be augmented further, by adding to it the negation of each detected claim. This presents two NLP problems: how to automatically negate a claim, and when such a negated claim can plausibly be used. We present first steps into solving both these problems, using a rule-based approach for the former and a statistical one towards the latter.
Context Dependent Claim Detection.
Noam Slonim, Ran Levy, Yonatan Bilu, Daniel Hershcovich and Ehud Aharoni.
COLING 2014 (long paper).

Anthology abstract bib

While discussing a concrete controversial topic, most humans will find it challenging to swiftly raise a diverse set of convincing and relevant claims that should set the basis of their arguments. Here, we formally define the challenging task of automatic claim detection in a given context and discuss its associated unique difficulties. Further, we outline a preliminary solution to this task, and assess its performance over annotated real world data, collected specifically for that purpose over hundreds of Wikipedia articles. We report promising results of a supervised learning approach, which is based on a cascade of classifiers designed to properly handle the skewed data which is inherent to the defined task. These results demonstrate the viability of the introduced task.
Claims on Demand–an Initial Demonstration of a System for Automatic Detection and Polarity Identification of Context Dependent Claims in Massive Corpora.
Ehud Aharoni, Carlos Alzate, Roy Bar-Haim, Yonatan Bilu, Lena Dankin, Iris Eiron, Daniel Hershcovich, Shay Hummel.
COLING 2014 (system demonstration).

Anthology abstract bib

While discussing a concrete controversial topic, most humans will find it challenging to swiftly raise a diverse set of convincing and relevant claims that should set the basis of their arguments. Here, we demonstrate the initial capabilities of a system that, given a controversial topic, can automatically pinpoint relevant claims in Wikipedia, determine their polarity with respect to the given topic, and articulate them per the user's request.
A Benchmark Dataset for Automatic Detection of Claims and Evidence in the Context of Controversial Topics.
Ehud Aharoni, Anatoly Polnarov, Tamar Lavee, Daniel Hershcovich, Ran Levy, Ruty Rinott, Dan Gutfreund, Noam Slonim.
Workshop on Argumentation Mining at ACL 2014.

Anthology abstract bib

We describe a novel and unique argumentative structure dataset. This corpus consists of data extracted fro m hundreds of Wikipedia articles using a meticulously monitored manual annotation process. The result is 2,683 argument elements, collected in the context of 33 controversial topics, organized under a simple claim-evidence structure. The obtained data are publicly available for academic research.
Verification of Transactional Memory in POWER8.
Alon Adir, Dave Goodman, Daniel Hershcovich, Oz Hershkovitz, Bryan Hickerson, Karen Holtz, Wisam Kadry, Anatoly Koyfman, John Ludden, Charles Meissner, Amir Nahir, Randall R Pratt, Mike Schiffli, Brett St Onge, Brian Thompto, Elena Tsanko, Avi Ziv.
DAC 2014.

pdf abstract bib

Transactional memory is a promising mechanism for synchronizing concurrent programs that eliminates locks at the expense of hardware complexity. Transactional memory is a hard feature to verify. First, transactions comprise several instructions that must be observed as a single global atomic operation. In addition, there are many reasons a transaction can fail. This results in a high level of non-determinism which must be tamed by the verification methodology. This paper describes the innovation that was applied to tools and methodology in pre-silicon simulation, acceleration and post-silicon in order to verify transactional memory in the IBM POWER8 processor core.

Broad-Coverage Semantic Parsing: A Transition-Based Approach
The Israeli Seminar on Computational Linguistics, ISCOL 2016 (05/2016)
Broad-Coverage Transition-Based UCCA Parsing
Hebrew University, Computer Science Learning Club (11/2016)
A Transition-Based Directed Acyclic Graph Parser for Universal Conceptual Cognitive Annotation
Technion, Computational Data Science Seminar (06/2017)
A Transition-Based Directed Acyclic Graph Parser for Universal Conceptual Cognitive Annotation
University of Washington, The Paul G. Allen Center for Computer Science & Engineering (07/2017)
A Transition-Based Directed Acyclic Graph Parser for Universal Conceptual Cognitive Annotation
Tel Aviv University, NLP Seminar (01/2018)
Universal Semantic Parsing with Neural Networks
Georgetown University (06/2019)

Human Language from a Computational Perspective (TA): 2014, 2015, 2016, 2017

Natural Language Processing (co-teaching): 2019

I co-organized ISCOL 2017, the annual meeting of the Israeli seminar on computational linguistics, as well as SemEval 2019 Task 1 on Cross-lingual Semantic Parsing with UCCA, and the CoNLL 2019 shared task on Cross-Framework Meaning Representation Parsing.
I served as a reviewer for ACL (2015, 2016, 2017, 2019), EMNLP (2017, 2018: Best Reviewer Award, 2019), IJCNLP 2017, COLING 2018, NAACL-HLT 2019, *SEM 2019, DMR 2019, NoDaLiDa 2019, EurNLP 2019 and AAAI 2020.

I practice Kendo (Japanese fencing). I was even at the World Championships in Japan (2015) and Korea (2018).

Daniel Hershcovich
Department of Computer Science
University of Copenhagen
Universitetsparken 1
DK-2100 Copenhagen Ø, Denmark
d... @di.ku.dk

Last update: 10/2019

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