pwc-archive/papers-with-abstracts huggingface dataset:
Modeling language constructs with fuzzy sets: some approaches, examples and interpretations
Authors: Pavlo Kapustin, Michael Kapustin
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5

We present and discuss a couple of approaches, including different types of projections, and some examples, discussing the use of fuzzy sets for modeling meaning of certain types of language constructs. We are mostly focusing on words other than adjectives and linguistic hedges as these categories are the most studied from before. We discuss logical and linguistic interpretations of membership functions. We argue that using fuzzy sets for modeling meaning of words and other natural language constructs, along with situations described with natural language is interesting both from purely linguistic perspective, and also as a knowledge representation for problems of computational linguistics and natural language processing.
Topological Data Analysis for Discourse Semantics?
Authors: Ketki Savle, Wlodek Zadrozny, Minwoo Lee
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5
Tasks: regression, Topological Data Analysis

In this paper we present new results on applying topological data analysis to discourse structures. We show that topological information, extracted from the relationships between sentences can be used in inference, namely it can be applied to the very difficult legal entailment given in the COLIEE 2018 data set. Previous results of Doshi and Zadrozny (2018) and Gholizadeh et al. (2018) show that topological features are useful for classification. The applications of computational topology to entailment are novel in our view provide a new set of tools for discourse semantics: computational topology can perhaps provide a bridge between the brittleness of logic and the regression of neural networks. We discuss the advantages and disadvantages of using topological information, and some open problems such as explainability of the classifier decisions.
Semantic Frame Embeddings for Detecting Relations between Software Requirements
Authors: Waad Alhoshan, Riza Batista-Navarro, Liping Zhao
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5
Tasks: Word Embeddings

The early phases of requirements engineering (RE) deal with a vast amount of software requirements (i.e., requirements that define characteristics of software systems), which are typically expressed in natural language. Analysing such unstructured requirements, usually obtained from users{'} inputs, is considered a challenging task due to the inherent ambiguity and inconsistency of natural language. To support such a task, methods based on natural language processing (NLP) can be employed. One of the more recent advances in NLP is the use of word embeddings for capturing contextual information, which can then be applied in word analogy tasks. In this paper, we describe a new resource, i.e., embedding-based representations of semantic frames in FrameNet, which was developed to support the detection of relations between software requirements. Our embeddings, which encapsulate contextual information at the semantic frame level, were trained on a large corpus of requirements (i.e., a collection of more than three million mobile application reviews). The similarity between these frame embeddings is then used as a basis for detecting semantic relatedness between software requirements. Compared with existing resources underpinned by word-level embeddings alone, and frame embeddings built upon pre-trained vectors, our proposed frame embeddings obtained better performance against judgements of an RE expert. These encouraging results demonstrate the strong potential of the resource in supporting RE analysis tasks (e.g., traceability), which we plan to investigate as part of our future work.
Detecting Collocations Similarity via Logical-Linguistic Model
Authors: Nina Khairova, Svitlana Petrasova, Orken Mamyrbayev, Kuralay Mukhsina
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5
Tasks: model, Semantic Similarity, Semantic Textual Similarity

Semantic similarity between collocations, along with words similarity, is one of the main issues of NLP, which must be addressed, in particular, in order to facilitate the automatic thesaurus generation. In the paper, we consider the logical-linguistic model that allows defining the relation of semantic similarity of collocations via the logical-algebraic equations. We provide the model for English, Ukrainian and Russian text corpora. The implementation for each language is slightly different in the equations of the finite predicates algebra and used linguistic resources. As a dataset for our experiment, we use 5801 pairs of sentences of Microsoft Research Paraphrase Corpus for English and more than 1 000 texts of scientific papers for Russian and Ukrainian.
Detecting Paraphrases of Standard Clause Titles in Insurance Contracts
Authors: Frieda Josi, Christian Wartena, Ulrich Heid
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Embeddings

For the analysis of contract texts, validated model texts, such as model clauses, can be used to identify reused contract clauses. This paper investigates how to calculate the similarity between titles of model clauses and headings extracted from contracts, and which similarity measure is most suitable for this. For the calculation of the similarities between title pairs we tested various variants of string similarity and token based similarity. We also compare two more semantic similarity measures based on word embeddings using pretrained embeddings and word embeddings trained on contract texts. The identification of the model clause title can be used as a starting point for the mapping of clauses found in contracts to verified clauses.
Semantic Matching of Documents from Heterogeneous Collections: A Simple and Transparent Method for Practical Applications
Authors: Mark-Christoph Mueller
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5
Tasks: Binary Classification

We present a very simple, unsupervised method for the pairwise matching of documents from heterogeneous collections. We demonstrate our method with the Concept-Project matching task, which is a binary classification task involving pairs of documents from heterogeneous collections. Although our method only employs standard resources without any domain- or task-specific modifications, it clearly outperforms the more complex system of the original authors. In addition, our method is transparent, because it provides explicit information about how a similarity score was computed, and efficient, because it is based on the aggregation of (pre-computable) word-level similarities.
Distribution is not enough: going Firther
Authors: Andy L{\"u}cking, Robin Cooper, Staffan Larsson, Jonathan Ginzburg
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5
Tasks: Word Embeddings

Much work in contemporary computational semantics follows the distributional hypothesis (DH), which is understood as an approach to semantics according to which the meaning of a word is a function of its distribution over contexts which is represented as vectors (word embeddings) within a multi-dimensional semantic space. In practice, use is identified with occurrence in text corpora, though there are some efforts to use corpora containing multi-modal information. In this paper we argue that the distributional hypothesis is intrinsically misguided as a self-supporting basis for semantics, as Firth was entirely aware. We mention philosophical arguments concerning the lack of normativity within DH data. Furthermore, we point out the shortcomings of DH as a model of learning, by discussing a variety of linguistic classes that cannot be learnt on a distributional basis, including indexicals, proper names, and wh-phrases. Instead of pursuing DH, we sketch an account of the problematic learning cases by integrating a rich, Firthian notion of dialogue context with interactive learning in signalling games backed by in probabilistic Type Theory with Records. We conclude that the success of the DH in computational semantics rests on a post hoc effect: DS presupposes a referential semantics on the basis of which utterances can be produced, comprehended and analysed in the first place.
Towards Natural Language Story Understanding with Rich Logical Schemas
Authors: Gene Louis Kim, Lane Lawley, Lenhart Schubert
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5

Generating {``}commonsense{'}{'} knowledge for intelligent understanding and reasoning is a difficult, long-standing problem, whose scale challenges the capacity of any approach driven primarily by human input. Furthermore, approaches based on mining statistically repetitive patterns fail to produce the rich representations humans acquire, and fall far short of human efficiency in inducing knowledge from text. The idea of our approach to this problem is to provide a learning system with a {``}head start{''} consisting of a semantic parser, some basic ontological knowledge, and most importantly, a small set of very general schemas about the kinds of patterns of events (often purposive, causal, or socially conventional) that even a one- or two-year-old could reasonably be presumed to possess. We match these initial schemas to simple children{'}s stories, obtaining concrete instances, and combining and abstracting these into new candidate schemas. Both the initial and generated schemas are specified using a rich, expressive logical form. While modern approaches to schema reasoning often only use slot-and-filler structures, this logical form allows us to specify complex relations and constraints over the slots. Though formal, the representations are language-like, and as such readily relatable to NL text. The agents, objects, and other roles in the schemas are represented by typed variables, and the event variables can be related through partial temporal ordering and causal relations. To match natural language stories with existing schemas, we first parse the stories into an underspecified variant of the logical form used by the schemas, which is suitable for most concrete stories. We include a walkthrough of matching a children{'}s story to these schemas and generating inferences from these matches.
Questions in Dependent Type Semantics
Authors: Kazuki Watanabe, Koji Mineshima, Daisuke Bekki
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5
Tasks: Natural Language Inference, RTE, Semantic Parsing, Vocal Bursts Type Prediction

Dependent Type Semantics (DTS; Bekki and Mineshima, 2017) is a proof-theoretic compositional dynamic semantics based on Dependent Type Theory. The semantic representations for declarative sentences in DTS are types, based on the propositions-as-types paradigm. While type-theoretic semantics for natural language based on dependent type theory has been developed by many authors, how to assign semantic representations to interrogative sentences has been a non-trivial problem. In this study, we show how to provide the semantics of interrogative sentences in DTS. The basic idea is to assign the same type to both declarative sentences and interrogative sentences, partly building on the recent proposal in Inquisitive Semantics. We use Combinatory Categorial Grammar (CCG) as a syntactic component of DTS and implement our compositional semantics for interrogative sentences using ccg2lambda, a semantic parsing platform based on CCG. Based on the idea that the relationship between questions and answers can be formulated as the task of Recognizing Textual Entailment (RTE), we implement our inference system using proof assistant Coq and show that our system can deal with a wide range of question-answer relationships discussed in the formal semantics literature, including those with polar questions, alternative questions, and wh-questions.
Monads for hyperintensionality? A situation semantics for hyperintensional side effects
Authors: Luke Burke
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5

We outline a hyperintensional situation semantics in which hyperintensionality is modelled as a {`}side effect{'}, as this term has been understood in natural language semantics and in functional programming. We use monads from category theory in order to {`}upgrade{'} an ordinary intensional semantics to a possible hyperintensional counterpart.
Transition-based DRS Parsing Using Stack-LSTMs
Authors: Kilian Evang
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5
Tasks: DRS Parsing, Semantic Parsing

We present our submission to the IWCS 2019 shared task on semantic parsing, a transition-based parser that uses explicit word-meaning pairings, but no explicit representation of syntax. Parsing decisions are made based on vector representations of parser states, encoded via stack-LSTMs (Ballesteros et al., 2017), as well as some heuristic rules. Our system reaches 70.88{\%} f-score in the competition.
Discourse Representation Structure Parsing with Recurrent Neural Networks and the Transformer Model
Authors: Jiangming Liu, Shay B. Cohen, Mirella Lapata
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5
Tasks: Decoder, DRS Parsing, Machine Translation, Translation

We describe the systems we developed for Discourse Representation Structure (DRS) parsing as part of the IWCS-2019 Shared Task of DRS Parsing.1 Our systems are based on sequence-to-sequence modeling. To implement our model, we use the open-source neural machine translation system implemented in PyTorch, OpenNMT-py. We experimented with a variety of encoder-decoder models based on recurrent neural networks and the Transformer model. We conduct experiments on the standard benchmark of the Parallel Meaning Bank (PMB 2.2). Our best system achieves a score of 84.8{\%} F1 in the DRS parsing shared task.
Neural Boxer at the IWCS Shared Task on DRS Parsing
Authors: Rik van Noord
Date: 2019-05-01 00:00:00
Proceeding: WS 2019 5
Tasks: DRS Parsing

This paper describes our participation in the shared task of Discourse Representation Structure parsing. It follows the work of Van Noord et al. (2018), who employed a neural sequence-to-sequence model to produce DRSs, also exploiting linguistic information with multiple encoders. We provide a detailed look in the performance of this model and show that (i) the benefit of the linguistic features is evident across a number of experiments which vary the amount of training data and (ii) the model can be improved by applying a number of postprocessing methods to fix ill-formed output. Our model ended up in second place in the competition, with an F-score of 84.5.
Bottom-Up Unranked Tree-to-Graph Transducers for Translation into Semantic Graphs
Authors: Johanna Bj{\"o}rklund, Shay B. Cohen, Frank Drewes, Giorgio Satta
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9
Tasks: ARC, Semantic Parsing, Translation

We propose a formal model for translating unranked syntactic trees, such as dependency trees, into semantic graphs. These tree-to-graph transducers can serve as a formal basis of transition systems for semantic parsing which recently have been shown to perform very well, yet hitherto lack formalization. Our model features {``}extended{''} rules and an arc-factored normal form, comes with an efficient translation algorithm, and can be equipped with weights in a straightforward manner.
On the Compression of Lexicon Transducers
Authors: Marco Cognetta, Cyril Allauzen, Michael Riley
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9

In finite-state language processing pipelines, a lexicon is often a key component. It needs to be comprehensive to ensure accuracy, reducing out-of-vocabulary misses. However, in memory-constrained environments (e.g., mobile phones), the size of the component automata must be kept small. Indeed, a delicate balance between comprehensiveness, speed, and memory must be struck to conform to device requirements while providing a good user experience.In this paper, we describe a compression scheme for lexicons when represented as finite-state transducers. We efficiently encode the graph of the transducer while storing transition labels separately. The graph encoding scheme is based on the LOUDS (Level Order Unary Degree Sequence) tree representation, which has constant time tree traversal for queries while being information-theoretically optimal in space. We find that our encoding is near the theoretical lower bound for such graphs and substantially outperforms more traditional representations in space while remaining competitive in latency benchmarks.
MSO with tests and reducts
Authors: Fern, Tim o, David Woods, Carl Vogel
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9

Tests added to Kleene algebra (by Kozen and others) are considered within Monadic Second Order logic over strings, where they are likened to statives in natural language. Reducts are formed over tests and non-tests alike, specifying what is observable. Notions of temporal granularity are based on observable change, under the assumption that a finite set bounds what is observable (with the possibility of stretching such bounds by moving to a larger finite set). String projections at different granularities are conjoined by superpositions that provide another variant of concatenation for Booleans.
Finite State Transducer Calculus for Whole Word Morphology
Authors: Maciej Janicki
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9
Tasks: Morphological Analysis

The research on machine learning of morphology often involves formulating morphological descriptions directly on surface forms of words. As the established two-level morphology paradigm requires the knowledge of the underlying structure, it is not widely used in such settings. In this paper, we propose a formalism describing structural relationships between words based on theories of morphology that reject the notions of internal word structure and morpheme. The formalism covers a wide variety of morphological phenomena (including non-concatenative ones like stem vowel alternation) without the need of workarounds and extensions. Furthermore, we show that morphological rules formulated in such way can be easily translated to FSTs, which enables us to derive performant approaches to morphological analysis, generation and automatic rule discovery.
Weighted parsing for grammar-based language models
Authors: Richard M{\"o}rbitz, Heiko Vogler
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9

We develop a general framework for weighted parsing which is built on top of grammar-based language models and employs flexible weight algebras. It generalizes previous work in that area (semiring parsing, weighted deductive parsing) and also covers applications outside the classical scope of parsing, e.g., algebraic dynamic programming. We show an algorithm which terminates and is correct for a large class of weighted grammar-based language models.
Regular transductions with MCFG input syntax
Authors: Mark-Jan Nederhof, Heiko Vogler
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9

We show that regular transductions for which the input part is generated by some multiple context-free grammar can be simulated by synchronous multiple context-free grammars. We prove that synchronous multiple context-free grammars are strictly more powerful than this combination of regular transductions and multiple context-free grammars.
A Syntactically Expressive Morphological Analyzer for Turkish
Authors: Adnan Ozturel, Tolga Kayadelen, Isin Demirsahin
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9
Tasks: Language Modeling, Language Modelling

We present a broad coverage model of Turkish morphology and an open-source morphological analyzer that implements it. The model captures intricacies of Turkish morphology-syntax interface, thus could be used as a baseline that guides language model development. It introduces a novel fine part-of-speech tagset, a fine-grained affix inventory and represents morphotactics without zero-derivations. The morphological analyzer is freely available. It consists of modular reusable components of human-annotated gold standard lexicons, implements Turkish morphotactics as finite-state transducers using OpenFst and morphophonemic processes as Thrax grammars.
Using Meta-Morph Rules to develop Morphological Analysers: A case study concerning Tamil
Authors: Kengatharaiyer Sarveswaran, Gihan Dias, Miriam Butt
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9
Tasks: MORPH, Morphological Analysis

This paper describes a new and larger coverage Finite-State Morphological Analyser (FSM) and Generator for the Dravidian language Tamil. The FSM has been developed in the context of computational grammar engineering, adhering to the standards of the ParGram effort. Tamil is a morphologically rich language and the interaction between linguistic analysis and formal implementation is complex, resulting in a challenging task. In order to allow the development of the FSM to focus more on the linguistic analysis and less on the formal details, we have developed a system of meta-morph(ology) rules along with a script which translates these rules into FSM processable representations. The introduction of meta-morph rules makes it possible for computationally naive linguists to interact with the system and to expand it in future work. We found that the meta-morph rules help to express linguistic generalisations and reduce the manual effort of writing lexical classes for morphological analysis. Our Tamil FSM currently handles mainly the inflectional morphology of 3,300 verb roots and their 260 forms. Further, it also has a lexicon of approximately 100,000 nouns along with a guesser to handle out-of-vocabulary items. Although the Tamil FSM was primarily developed to be part of a computational grammar, it can also be used as a web or stand-alone application for other NLP tasks, as per general ParGram practice.
Distilling weighted finite automata from arbitrary probabilistic models
Authors: An Suresh, a Theertha, Brian Roark, Michael Riley, Vlad Schogol
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9

Weighted finite automata (WFA) are often used to represent probabilistic models, such as n-gram language models, since they are efficient for recognition tasks in time and space. The probabilistic source to be represented as a WFA, however, may come in many forms. Given a generic probabilistic model over sequences, we propose an algorithm to approximate it as a weighted finite automaton such that the Kullback-Leibler divergence between the source model and the WFA target model is minimized. The proposed algorithm involves a counting step and a difference of convex optimization, both of which can be performed efficiently. We demonstrate the usefulness of our approach on some tasks including distilling n-gram models from neural models.
Silent HMMs: Generalized Representation of Hidden Semi-Markov Models and Hierarchical HMMs
Authors: Kei Wakabayashi
Date: 2019-09-01 00:00:00
Proceeding: WS 2019 9

Modeling sequence data using probabilistic finite state machines (PFSMs) is a technique that analyzes the underlying dynamics in sequences of symbols. Hidden semi-Markov models (HSMMs) and hierarchical hidden Markov models (HHMMs) are PFSMs that have been successfully applied to a wide variety of applications by extending HMMs to make the extracted patterns easier to interpret. However, these models are independently developed with their own training algorithm, so that we cannot combine multiple kinds of structures to build a PFSM for a specific application. In this paper, we prove that silent hidden Markov models (silent HMMs) are flexible models that have more expressive power than HSMMs and HHMMs. Silent HMMs are HMMs that contain silent states, which do not emit any observations. We show that we can obtain silent HMM equivalent to given HSMMs and HHMMs. We believe that these results form a firm foundation to use silent HMMs as a unified representation for PFSM modeling.