Tutorial at LREC 2020 Graph-Based Meaning Representations: Design and Processing
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Tutorial at LREC 2020
Graph-Based Meaning Representations:
Design and Processing
https://github.com/cfmrp/tutorial
Alexander Koller Stephan Oepen Weiwei Sun
Saarland University University of Oslo Peking University
koller@coli.uni-saarland.de oe@ifi.uio.no ws@pku.edu.cn
Abstract our research community by providing a unifying
view on these graph banks and their associated
This tutorial is on representing and process-
ing sentence meaning in the form of labeled parsing problems, while working out similarities
directed graphs. The tutorial will (a) briefly and differences between common frameworks and
review relevant background in formal and lin- techniques.
guistic semantics; (b) semi-formally define a Based on common-sense linguistic and formal
unified abstract view on different flavors of se- dimensions established in its first part, the tutorial
mantic graphs and associated terminology; (c)
will provide a coherent, systematized overview of
survey common frameworks for graph-based
meaning representation and available graph
this field. Participants will be enabled to identify
banks; and (d) offer a technical overview of genuine content differences between frameworks
a representative selection of different parsing as well as to tease apart more superficial variation,
approaches. for example in terminology or packaging. Fur-
thermore, major current processing techniques for
1 Tutorial Content and Relevance semantic graphs will be reviewed against a high-
All things semantic have been receiving height- level inventory of families of approaches. This part
ened attention in recent years. Despite remarkable of the tutorial will emphasize reflections on co-
advances in vector-based (continuous, dense, and dependencies with specific graph flavors or frame-
distributed) encodings of meaning, ‘classic’ (hier- works, on worst-case and typical time and space
archically structured and discrete) semantic rep- complexity, as well as on what guarantees (if any)
resentations continue to play an important role in are obtained on the wellformedness and correct-
‘making sense’ of natural language. While parsing ness of output structures.
has long been dominated by tree-structured target Kate and Wong (2010) suggest a definition of
representations, there is now growing interest in semantic parsing as “the task of mapping natural
general graphs as more expressive and arguably language sentences into complete formal mean-
more adequate target structures for sentence-level ing representations which a computer can execute
grammatical analysis beyond surface syntax and in for some domain-specific application.” This view
particular for the representation of semantic struc- brings along a tacit expectation to map (more or
ture. less) directly from a linguistic surface form to an
Today, the landscape of meaning representation actionable encoding of its intended meaning, e.g.
approaches, annotated graph banks, and parsing in a database query or even programming lan-
techniques into these structures is complex and di- guage. In this tutorial, we embrace a broader per-
verse. Graph-based semantic parsing has been a spective on semantic parsing as it has come to be
task in almost every Semantic Evaluation (Sem- viewed commonly in recent years. We will review
Eval) exercise since 2014. These shared tasks graph-based meaning representations that aim to
were based on a variety of different corpora with be application- and domain-independent, i.e. seek
graph-based meaning annotations (graph banks), to provide a reusable intermediate layer of inter-
which differ both in their formal properties and in pretation that captures, in suitably abstract form,
the facets of meaning they aim to represent. The relevant constraints that the linguistic signal im-
goal of this tutorial is to clarify this landscape for poses on interpretation.Tutorial slides and additional materials are 2008), DELPH-IN MRS Bi-Lexical Dependencies
available at the following address: (DM; Ivanova et al., 2012) and Prague Semantic
https://github.com/cfmrp/tutorial Dependencies (PSD; a simplification of the tecto-
grammatical structures of Hajič et al., 2012).
2 Semantic Graph Banks
In the first part of the tutorial, we will give a sys-
tematic overview of the available semantic graph Type (1) A more general form of anchored se-
banks. On the one hand, we will distinguish graph mantic graphs is characterized by relaxing the
banks with respect to the facets of natural language correspondence relations between nodes and to-
meaning they aim to represent. For instance, some kens, while still explicitly annotating the corre-
graph banks focus on predicate–argument struc- spondence between nodes and parts of the sen-
ture, perhaps with some extensions for polarity or tence. Some graph banks of this flavor align nodes
tense, whereas others capture (some) scopal phe- with arbitrary parts of the sentence, including sub-
nomena. Furthermore, while the graphs in most token or multi-token sequences, which affords
graph banks do not have a precisely defined model more flexibility in the representation of meaning
theory in the sense of classical linguistic seman- contributed by, for example, (derivational) affixes
tics, there are still underlying intuitions about what or phrasal constructions. Some further allow mul-
the nodes of the graphs mean (individual entities tiple nodes to correspond to overlapping spans,
and eventualities in the world vs. more abstract ob- enabling lexical decomposition (e.g. of causatives
jects to which statements about scope and presup- or comparatives). Frameworks instantiating this
position can attach). We will discuss the different flavor of semantic graphs include Universal Con-
intuitions that underly different graph banks. ceptual Cognitive Annotation (UCCA; Abend and
On the other hand, we will follow Kuhlmann Rappoport, 2013; featured in a SemEval 2019
and Oepen (2016) in classifying graph banks with task) and two variants of ‘reducing’ the under-
respect to the relationship they assume between specified logical forms of Flickinger (2000) and
the tokens of the sentence and the nodes of the Copestake et al. (2005) into directed graphs, viz.
graph (called anchoring of graph fragments onto Elementary Dependency Structures (EDS; Oepen
input sub-strings). We will distinguish three fla- and Lønning, 2006) and Dependency Minimal Re-
vors of semantic graphs, which by degree of an- cursion Semantics (DMRS; Copestake, 2009). All
choring we will call type (0) to type (2). While we three frameworks serve as target representations in
use ‘flavor’ to refer to formally defined sub-classes recent parsing research (e.g. Buys and Blunsom,
of semantic graphs, we will reserve the term 2017; Chen et al., 2018; Hershcovich et al., 2018).
‘framework’ for a specific linguistic approach
to graph-based meaning representation (typically
cast in a particular graph flavor, of course).
Type (2) Finally, our framework review will in-
Type (0) The strongest form of anchoring is clude Abstract Meaning Representation (AMR;
obtained in bi-lexical dependency graphs, where Banarescu et al., 2013), which in our hierarchy of
graph nodes injectively correspond to surface lex- graph flavors is considered unanchored, in that the
ical units (tokens). In such graphs, each node correspondence between nodes and tokens is not
is directly linked to a specific token (conversely, explicitly annotated. The AMR framework de-
there may be semantically empty tokens), and the liberately backgrounds notions of compositional-
nodes inherit the linear order of their correspond- ity and derivation. At the same time, AMR fre-
ing tokens. This flavor of semantic graphs was quently invokes lexical decomposition and repre-
popularized in part through a series of Seman- sents some implicitly expressed elements of mean-
tic Dependency Parsing (SDP) tasks at the Se- ing, such that AMR graphs quite generally appear
mEval exercises in 2014–16 (Oepen et al., 2014, to ‘abstract’ furthest from the surface signal. Since
2015; Che et al., 2016). Prominent linguistic the first general release of an AMR graph bank in
frameworks instantiating this graph flavor include 2014, the framework has provided a popular target
CCG word–word dependencies (CCD; Hocken- for semantic parsing and has been the subject of
maier and Steedman, 2007), Enju Predicate– two consecutive tasks at SemEval 2016 and 2017
Argument Structures (PAS; Miyao and Tsujii, (May, 2016; May and Priyadarshi, 2017).3 Processing Semantic Graphs 4 Tutorial Structure
The creation of large-scale, high-quality seman- We have organized the content of the tutorial into
tic graph banks has driven research on semantic the following blocks, which add up to a total of
parsing, where a system is trained to map from three hours of presentation. The references be-
natural-language sentences to graphs. There is low are illustrative of the content in each block;
now a dizzying array of different semantic pars- in the tutorial itself, we will present one or two ap-
ing algorithms, and it is a challenge to keep track proaches per block in detail while treating others
of their respective strengths and weaknesses. Dif- more superficially.
ferent parsing approaches are, of course, more or (1) Linguistic Foundations: Layers of Sentence
less effective for graph banks of different flavors Meaning
(and, at times, even specific frameworks). We will
discuss these interactions in the tutorial and cate- (2) Formal Foundations: Labeled Directed
gorize existing approaches into four classes. Graphs
(3) Meaning Representation Frameworks and
Factorization-based approach A factorization- Graph Banks
based parser explicitly models the target seman-
tic structures by defining a score function that is • Bi-Lexical semantic dependencies (Hocken-
able to evaluate the “goodness” of any candidate maier and Steedman, 2007; Miyao and Tsu-
graph. To make a score function computable, a jii, 2008; Hajič et al., 2012; Ivanova et al.,
parser usually factorizes the score of a graph into 2012; Che et al., 2016);
parts for smaller substrings and can then apply dy-
• Universal Conceptual Cognitive Annotation
namic programming to search for the best graph.
(UCCA; Abend and Rappoport, 2013);
Composition-based approach Following the • Graph-Based Minimal Recursion Semantics
Principle of Compositionality, a semantic graph (EDS and DMRS; Oepen and Lønning,
can be viewed as the result of a derivation pro- 2006; Copestake, 2009);
cess, in which a set of lexical and syntactico-
semantic rules are iteratively applied and evalu- • Abstract Meaning Representation (AMR;
ated. A composition-based parser explicitly mod- Banarescu et al., 2013);
els such derivation structures by defining a sym- • Non-Graph Representations: Discourse Rep-
bolic system to manipulate graph construction and resentation Structures (DRS; Basile et al.,
a score function to select preferable derivations. 2012);
Transition-based approach A transition-based • Contrastive review of selected examples
parser models a derivation process in a left-to- across frameworks;
right, word-by-word way. The key to building a
• Availability of training and evaluation data;
high-accuracy parser is to define a score function
shared tasks; state-of-the-art empirical results
that evaluates the individual derivation decisions
(Oepen et al., 2019).
for each token. In order to find a good derivation
among a large set, a parser usually adopts a greedy (4) Parsing into Semantic Graphs
search strategy which is sometimes psycholinguis-
tically motivated. • Parser evaluation: quantifying semantic
graph similarity;
Translation-based approach A translation- • Parsing sub-tasks: segmentation, concept
based parser takes a family of semantic graphs identification, relation detection, structural
as a foreign language, in that a semantic graph is validation;
encoded into a string and then viewed as a “sen-
tence” from a different language. By linearizing • Composition-based methods (Callmeier,
a graph into a string, a parser can reuse various 2000; Bos et al., 2004; Artzi et al., 2015;
successful seq2seq models that are the heart of Groschwitz et al., 2018; Lindemann et al.,
modern Neural Machine Translation. 2019; Chen et al., 2018);• Factorization-based methods (Flanigan http://www.coli.uni-saarland.de/
et al., 2014; Kuhlmann and Jonsson, 2015; ~koller
Peng et al., 2017; Dozat and Manning, Alexander Koller received his PhD in 2004, with
2018); a thesis on underspecified processing of seman-
• Transition-based methods (Sagae and Tsu- tic ambiguities using graph-based representations.
jii, 2008; Wang et al., 2015; Buys and Blun- His research interests span a variety of topics in-
som, 2017; Hershcovich et al., 2017); cluding parsing, generation, the expressive capac-
ity of representation formalisms for natural lan-
• Translation-based methods (Konstas et al., guage, and semantics. Within semantics, he has
2017; Peng et al., 2018; Stanovsky and Da- published extensively on semantic parsing using
gan, 2018); both grammar-based and neural approaches. His
most recent work in this field (Lindemann et al.,
• Cross-framework parsing and multi-task
2019) achieved state-of-the-art semantic parsing
learning (Peng et al., 2017; Hershcovich
accuracy across several graphbanks using neural
et al., 2018; Stanovsky and Dagan, 2018);
supertagging and dependency in the context of a
• Cross-lingual parsing methods (Evang and compositional model.
Bos, 2016; Damonte and Cohen, 2018; Stephan Oepen
Zhang et al., 2018); Department of Informatics, University of Oslo,
Norway
• Contrastive discussion across frameworks,
oe@ifi.uio.no
approaches, and languages.
https://www.mn.uio.no/ifi/
(5) Outlook: Applications of Semantic Graphs english/people/aca/oe/
5 Content Breadth Stephan Oepen studied Linguistics, German and
Russian Philology, Computer Science, and Com-
Each of us has contributed research to the design putational Linguistics at Berlin, Volgograd, and
of meaning representation frameworks, creation Saarbrücken. He has worked extensively on
of semantic graph banks, and and/or the develop- constraint-based parsing and realization, on the
ment of meaning representation parsing systems. design of broad-coverage meaning representa-
Nonetheless, both the design and the processing of tions and the syntax–semantics interface, and on
graph banks are highly active research areas, and the use of syntactico-semantic structure in natu-
our own work will not represent more than a fifth ral language understanding applications. He has
of the total tutorial content. been a co-developer of the LinGO English Re-
source Grammar (ERG) since the mid-1990s, has
6 Participant Background helped create the Redwoods Treebank of scope-
An understanding of basic parsing techniques underspecified MRS meaning representations, and
(chart-based and transition-based) and a familiar- has chaired two SemEval tasks on Semantic De-
ity with basic neural techniques (feed-forward and pendency Parsing as well as the First Shared
recurrent networks, encoder–decoder) will be use- Task on Cross-Framework Meaning Representa-
ful. tion Parsing (MRP) at the 2019 Conference for
Computational Language Learning.
7 Presenters
Weiwei Sun
The tutorial will be given jointly by three presen- Institute of Computer Science and Technology,
ters with partly overlapping and partly comple- Peking University, China
mentary expertise. Each will contribute about one ws@pku.edu.cn
third of the content, and each will be involved in https://wsun106.github.io/
multiple parts of the tutorial. Weiwei Sun completed her Ph.D. in the Depart-
Alexander Koller ment of Computational Linguistics from Saarland
Department of Language Science and University under the supervision of Prof. Hans
Technology, Saarland University, Germany Uszkoreit. Before that, she studied at Peking Uni-
koller@coli.uni-saarland.de versity, where she obtained BA in Linguistics, andBS and MS in Computer Science. Her research Wanxiang Che, Yanqiu Shao, Ting Liu, and Yu Ding.
lies at the intersection of computational linguistics 2016. SemEval-2016 task 9: Chinese semantic de-
pendency parsing. In Proceedings of the 10th Inter-
and natural language processing. The main topic
national Workshop on Semantic Evaluation, pages
is symbolic and statistical parsing, with a special 1074 – 1080, San Diego, CA, USA.
focus on parsing into semantic graphs of various
flavors. She has repeatedly chaired teams that Yufei Chen, Weiwei Sun, and Xiaojun Wan. 2018. Ac-
curate SHRG-based semantic parsing. In Proceed-
have submitted top-performing systems to recent ings of the 56th Meeting of the Association for Com-
SemEval shared tasks and has continuously ad- putational Linguistics, pages 408 – 418, Melbourne,
vanced both the state of the art in semantic parsing Australia.
in terms of empirical results and the understand-
Ann Copestake. 2009. Slacker semantics. Why super-
ing of how design decisions in different schools of ficiality, dependency and avoidance of commitment
linguistic graph representations impact formal and can be the right way to go. In Proceedings of the
algorithmic complexity. 12th Meeting of the European Chapter of the Asso-
ciation for Computational Linguistics, pages 1 – 9,
Athens, Greece.
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