pwc-archive/papers-with-abstracts huggingface dataset:
NLP-Cube: End-to-End Raw Text Processing With Neural Networks
Authors: Tiberiu Boros, Stefan Daniel Dumitrescu, Rux Burtica, ra
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Lemmatization, Sentence

We introduce NLP-Cube: an end-to-end Natural Language Processing framework, evaluated in CoNLL{'}s {``}Multilingual Parsing from Raw Text to Universal Dependencies 2018{''} Shared Task. It performs sentence splitting, tokenization, compound word expansion, lemmatization, tagging and parsing. Based entirely on recurrent neural networks, written in Python, this ready-to-use open source system is freely available on GitHub. For each task we describe and discuss its specific network architecture, closing with an overview on the results obtained in the competition.
CUNI x-ling: Parsing Under-Resourced Languages in CoNLL 2018 UD Shared Task
Authors: Rudolf Rosa, David Mare{\v{c}}ek
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Translation

This is a system description paper for the CUNI x-ling submission to the CoNLL 2018 UD Shared Task. We focused on parsing under-resourced languages, with no or little training data available. We employed a wide range of approaches, including simple word-based treebank translation, combination of delexicalized parsers, and exploitation of available morphological dictionaries, with a dedicated setup tailored to each of the languages. In the official evaluation, our submission was identified as the clear winner of the Low-resource languages category.
UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task
Authors: Milan Straka
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Dependency Parsing, Lemmatization, POS, POS Tagging, Sentence, Sentence segmentation, Word Embeddings

UDPipe is a trainable pipeline which performs sentence segmentation, tokenization, POS tagging, lemmatization and dependency parsing. We present a prototype for UDPipe 2.0 and evaluate it in the CoNLL 2018 UD Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, which employs three metrics for submission ranking. Out of 26 participants, the prototype placed first in the MLAS ranking, third in the LAS ranking and third in the BLEX ranking. In extrinsic parser evaluation EPE 2018, the system ranked first in the overall score.
Universal Morpho-Syntactic Parsing and the Contribution of Lexica: Analyzing the ONLP Lab Submission to the CoNLL 2018 Shared Task
Authors: Amit Seker, Amir More, Reut Tsarfaty
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10

We present the contribution of the ONLP lab at the Open University of Israel to the UD shared task on multilingual parsing from raw text to Universal Dependencies. Our contribution is based on a transition-based parser called {`}yap {--} yet another parser{'}, which includes a standalone morphological model, a standalone dependency model, and a joint morphosyntactic model. In the task we used \textit{yap}{`}s standalone dependency parser to parse input morphologically disambiguated by UDPipe, and obtained the official score of 58.35 LAS. In our follow up investigation we use yap to show how the incorporation of morphological and lexical resources may improve the performance of end-to-end raw-to-dependencies parsing in the case of a \textit{morphologically-rich} and \textit{low-resource} language, Modern Hebrew. Our results on Hebrew underscore the importance of CoNLL-UL, a UD-compatible standard for accessing external lexical resources, for enhancing end-to-end UD parsing, in particular for morphologically rich and low-resource languages. We thus encourage the community to create, convert, or make available more such lexica in future tasks.
SParse: Ko\cc University Graph-Based Parsing System for the CoNLL 2018 Shared Task
Authors: Berkay {\"O}nder, Can G{\"u}meli, Deniz Yuret
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Dependency Parsing, Language Modeling, Language Modelling, Meta-Learning, Word Embeddings

We present SParse, our Graph-Based Parsing model submitted for the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies (Zeman et al., 2018). Our model extends the state-of-the-art biaffine parser (Dozat and Manning, 2016) with a structural meta-learning module, SMeta, that combines local and global label predictions. Our parser has been trained and run on Universal Dependencies datasets (Nivre et al., 2016, 2018) and has 87.48{\%} LAS, 78.63{\%} MLAS, 78.69{\%} BLEX and 81.76{\%} CLAS (Nivre and Fang, 2017) score on the Italian-ISDT dataset and has 72.78{\%} LAS, 59.10{\%} MLAS, 61.38{\%} BLEX and 61.72{\%} CLAS score on the Japanese-GSD dataset in our official submission. All other corpora are evaluated after the submission deadline, for whom we present our unofficial test results.
ELMoLex: Connecting ELMo and Lexicon Features for Dependency Parsing
Authors: Ganesh Jawahar, Benjamin Muller, Amal Fethi, Louis Martin, {\'E}ric Villemonte de la Clergerie, Beno{\^\i}t Sagot, Djam{\'e} Seddah
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Dependency Parsing, Language Modeling, Language Modelling

In this paper, we present the details of the neural dependency parser and the neural tagger submitted by our team {`}ParisNLP{'} to the CoNLL 2018 Shared Task on parsing from raw text to Universal Dependencies. We augment the deep Biaffine (BiAF) parser (Dozat and Manning, 2016) with novel features to perform competitively: we utilize an indomain version of ELMo features (Peters et al., 2018) which provide context-dependent word representations; we utilize disambiguated, embedded, morphosyntactic features from lexicons (Sagot, 2018), which complements the existing feature set. Henceforth, we call our system {`}ELMoLex{'}. In addition to incorporating character embeddings, ELMoLex benefits from pre-trained word vectors, ELMo and morphosyntactic features (whenever available) to correctly handle rare or unknown words which are prevalent in languages with complex morphology. ELMoLex ranked 11th by Labeled Attachment Score metric (70.64{\%}), Morphology-aware LAS metric (55.74{\%}) and ranked 9th by Bilexical dependency metric (60.70{\%}).
A Morphology-Based Representation Model for LSTM-Based Dependency Parsing of Agglutinative Languages
Authors: {\c{S}}aziye Bet{\"u}l {\"O}zate{\c{s}}, Arzucan {\"O}zg{\"u}r, Tunga G{\"u}ng{\"o}r, Balk{\i}z {\"O}zt{\"u}rk
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Dependency Parsing, Word Embeddings

We propose two word representation models for agglutinative languages that better capture the similarities between words which have similar tasks in sentences. Our models highlight the morphological features in words and embed morphological information into their dense representations. We have tested our models on an LSTM-based dependency parser with character-based word embeddings proposed by Ballesteros et al. (2015). We participated in the CoNLL 2018 Shared Task on multilingual parsing from raw text to universal dependencies as the BOUN team. We show that our morphology-based embedding models improve the parsing performance for most of the agglutinative languages.
AntNLP at CoNLL 2018 Shared Task: A Graph-Based Parser for Universal Dependency Parsing
Authors: Tao Ji, Yufang Liu, Yijun Wang, Yuanbin Wu, Man Lan
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Dependency Parsing

We describe the graph-based dependency parser in our system (AntNLP) submitted to the CoNLL 2018 UD Shared Task. We use bidirectional lstm to get the word representation, then a bi-affine pointer networks to compute scores of candidate dependency edges and the MST algorithm to get the final dependency tree. From the official testing results, our system gets 70.90 LAS F1 score (rank 9/26), 55.92 MLAS (10/26) and 60.91 BLEX (8/26).
A Simple yet Effective Joint Training Method for Cross-Lingual Universal Dependency Parsing
Authors: Danlu Chen, Mengxiao Lin, Zhifeng Hu, Xipeng Qiu
Date: 2018-10-01 00:00:00
Proceeding: CONLL 2018 10
Tasks: Dependency Parsing, Transfer Learning

This paper describes Fudan{'}s submission to CoNLL 2018{'}s shared task Universal Dependency Parsing. We jointly train models when two languages are similar according to linguistic typology and then ensemble the models using a simple re-parse algorithm. We outperform the baseline method by 4.4{\%} (2.1{\%}) on average on development (test) set in CoNLL 2018 UD Shared Task.
On the Derivational Entropy of Left-to-Right Probabilistic Finite-State Automata and Hidden Markov Models
Authors: Joan Andreu S{\'a}nchez, Martha Alicia Rocha, Ver{\'o}nica Romero, Mauricio Villegas
Date: 2018-03-01 00:00:00
Proceeding: CL 2018 3
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Probabilistic finite-state automata are a formalism that is widely used in many problems of automatic speech recognition and natural language processing. Probabilistic finite-state automata are closely related to other finite-state models as weighted finite-state automata, word lattices, and hidden Markov models. Therefore, they share many similar properties and problems. Entropy measures of finite-state models have been investigated in the past in order to study the information capacity of these models. The derivational entropy quantifies the uncertainty that the model has about the probability distribution it represents. The derivational entropy in a finite-state automaton is computed from the probability that is accumulated in all of its individual state sequences. The computation of the entropy from a weighted finite-state automaton requires a normalized model. This article studies an efficient computation of the derivational entropy of left-to-right probabilistic finite-state automata, and it introduces an efficient algorithm for normalizing weighted finite-state automata. The efficient computation of the derivational entropy is also extended to continuous hidden Markov models.
A Notion of Semantic Coherence for Underspecified Semantic Representation
Authors: Mehdi Manshadi, Daniel Gildea, James F. Allen
Date: 2018-03-01 00:00:00
Proceeding: CL 2018 3
Tasks: Sentence

The general problem of finding satisfying solutions to constraint-based underspecified representations of quantifier scope is NP-complete. Existing frameworks, including Dominance Graphs, Minimal Recursion Semantics, and Hole Semantics, have struggled to balance expressivity and tractability in order to cover real natural language sentences with efficient algorithms. We address this trade-off with a general principle of coherence, which requires that every variable introduced in the domain of discourse must contribute to the overall semantics of the sentence. We show that every underspecified representation meeting this criterion can be efficiently processed, and that our set of representations subsumes all previously identified tractable sets.
Cache Transition Systems for Graph Parsing
Authors: Daniel Gildea, Giorgio Satta, Xiaochang Peng
Date: 2018-03-01 00:00:00
Proceeding: CL 2018 3
Tasks: Dependency Parsing, Semantic Parsing, Transition-Based Dependency Parsing, Tree Decomposition

Motivated by the task of semantic parsing, we describe a transition system that generalizes standard transition-based dependency parsing techniques to generate a graph rather than a tree. Our system includes a cache with fixed size m, and we characterize the relationship between the parameter m and the class of graphs that can be produced through the graph-theoretic concept of tree decomposition. We find empirically that small cache sizes cover a high percentage of sentences in existing semantic corpora.
Weighted DAG Automata for Semantic Graphs
Authors: David Chiang, Frank Drewes, Daniel Gildea, Adam Lopez, Giorgio Satta
Date: 2018-03-01 00:00:00
Proceeding: CL 2018 3

Graphs have a variety of uses in natural language processing, particularly as representations of linguistic meaning. A deficit in this area of research is a formal framework for creating, combining, and using models involving graphs that parallels the frameworks of finite automata for strings and finite tree automata for trees. A possible starting point for such a framework is the formalism of directed acyclic graph (DAG) automata, defined by Kamimura and Slutzki and extended by Quernheim and Knight. In this article, we study the latter in depth, demonstrating several new results, including a practical recognition algorithm that can be used for inference and learning with models defined on DAG automata. We also propose an extension to graphs with unbounded node degree and show that our results carry over to the extended formalism.
A Dependency Perspective on RST Discourse Parsing and Evaluation
Authors: Mathieu Morey, Philippe Muller, Nicholas Asher
Date: 2018-06-01 00:00:00
Proceeding: CL 2018 6
Tasks: Constituency Parsing, Dependency Parsing, Discourse Parsing

Computational text-level discourse analysis mostly happens within Rhetorical Structure Theory (RST), whose structures have classically been presented as constituency trees, and relies on data from the RST Discourse Treebank (RST-DT); as a result, the RST discourse parsing community has largely borrowed from the syntactic constituency parsing community. The standard evaluation procedure for RST discourse parsers is thus a simplified variant of PARSEVAL, and most RST discourse parsers use techniques that originated in syntactic constituency parsing. In this article, we isolate a number of conceptual and computational problems with the constituency hypothesis. We then examine the consequences, for the implementation and evaluation of RST discourse parsers, of adopting a dependency perspective on RST structures, a view advocated so far only by a few approaches to discourse parsing. While doing that, we show the importance of the notion of headedness of RST structures. We analyze RST discourse parsing as dependency parsing by adapting to RST a recent proposal in syntactic parsing that relies on head-ordered dependency trees, a representation isomorphic to headed constituency trees. We show how to convert the original trees from the RST corpus, RST-DT, and their binarized versions used by all existing RST parsers to head-ordered dependency trees. We also propose a way to convert existing simple dependency parser output to constituent trees. This allows us to evaluate and to compare approaches from both constituent-based and dependency-based perspectives in a unified framework, using constituency and dependency metrics. We thus propose an evaluation framework to compare extant approaches easily and uniformly, something the RST parsing community has lacked up to now. We can also compare parsers{'} predictions to each other across frameworks. This allows us to characterize families of parsing strategies across the different frameworks, in particular with respect to the notion of headedness. Our experiments provide evidence for the conceptual similarities between dependency parsers and shift-reduce constituency parsers, and confirm that dependency parsing constitutes a viable approach to RST discourse parsing.
Unrestricted Bridging Resolution
Authors: Yufang Hou, Katja Markert, Michael Strube
Date: 2018-06-01 00:00:00
Proceeding: CL 2018 6
Tasks: General Classification

In contrast to identity anaphors, which indicate coreference between a noun phrase and its antecedent, bridging anaphors link to their antecedent(s) via lexico-semantic, frame, or encyclopedic relations. Bridging resolution involves recognizing bridging anaphors and finding links to antecedents. In contrast to most prior work, we tackle both problems. Our work also follows a more wide-ranging definition of bridging than most previous work and does not impose any restrictions on the type of bridging anaphora or relations between anaphor and antecedent. We create a corpus (ISNotes) annotated for information status (IS), bridging being one of the IS subcategories. The annotations reach high reliability for all categories and marginal reliability for the bridging subcategory. We use a two-stage statistical global inference method for bridging resolution. Given all mentions in a document, the first stage, bridging anaphora recognition, recognizes bridging anaphors as a subtask of learning fine-grained IS. We use a cascading collective classification method where (i) collective classification allows us to investigate relations among several mentions and autocorrelation among IS classes and (ii) cascaded classification allows us to tackle class imbalance, important for minority classes such as bridging. We show that our method outperforms current methods both for IS recognition overall as well as for bridging, specifically. The second stage, bridging antecedent selection, finds the antecedents for all predicted bridging anaphors. We investigate the phenomenon of semantically or syntactically related bridging anaphors that share the same antecedent, a phenomenon we call sibling anaphors. We show that taking sibling anaphors into account in a joint inference model improves antecedent selection performance. In addition, we develop semantic and salience features for antecedent selection and suggest a novel method to build the candidate antecedent list for an anaphor, using the discourse scope of the anaphor. Our model outperforms previous work significantly.
Spurious Ambiguity and Focalization
Authors: Glyn Morrill, Oriol Valent{\'\i}n
Date: 2018-06-01 00:00:00
Proceeding: CL 2018 6

Spurious ambiguity is the phenomenon whereby distinct derivations in grammar may assign the same structural reading, resulting in redundancy in the parse search space and inefficiency in parsing. Understanding the problem depends on identifying the essential mathematical structure of derivations. This is trivial in the case of context free grammar, where the parse structures are ordered trees; in the case of type logical categorial grammar, the parse structures are proof nets. However, with respect to multiplicatives, intrinsic proof nets have not yet been given for displacement calculus, and proof nets for additives, which have applications to polymorphism, are not easy to characterize. In this context we approach here multiplicative-additive spurious ambiguity by means of the proof-theoretic technique of focalization.
The Influence of Context on the Learning of Metrical Stress Systems Using Finite-State Machines
Authors: Cesko Voeten, Menno van Zaanen
Date: 2018-06-01 00:00:00
Proceeding: CL 2018 6

Languages vary in the way stress is assigned to syllables within words. This article investigates the learnability of stress systems in a wide range of languages. The stress systems can be described using finite-state automata with symbols indicating levels of stress (primary, secondary, or no stress). Finite-state automata have been the focus of research in the area of grammatical inference for some time now. It has been shown that finite-state machines are learnable from examples using state-merging. One such approach, which aims to learn k-testable languages, has been applied to stress systems with some success. The family of k-testable languages has been shown to be efficiently learnable (in polynomial time). Here, we extend this approach to k, l-local languages by taking not only left context, but also right context, into account. We consider empirical results testing the performance of our learner using various amounts of context (corresponding to varying definitions of phonological locality). Our results show that our approach of learning stress patterns using state-merging is more reliant on left context than on right context. Additionally, some stress systems fail to be learned by our learner using either the left-context k-testable or the left-and-right-context k, l-local learning system. A more complex merging strategy, and hence grammar representation, is required for these stress systems.
Tree Structured Dirichlet Processes for Hierarchical Morphological Segmentation
Authors: Burcu Can, Man, Suresh har
Date: 2018-06-01 00:00:00
Proceeding: CL 2018 6
Tasks: Clustering, Information Retrieval, Machine Translation, Question Answering, Segmentation

This article presents a probabilistic hierarchical clustering model for morphological segmentation. In contrast to existing approaches to morphology learning, our method allows learning hierarchical organization of word morphology as a collection of tree structured paradigms. The model is fully unsupervised and based on the hierarchical Dirichlet process. Tree hierarchies are learned along with the corresponding morphological paradigms simultaneously. Our model is evaluated on Morpho Challenge and shows competitive performance when compared to state-of-the-art unsupervised morphological segmentation systems. Although we apply this model for morphological segmentation, the model itself can also be used for hierarchical clustering of other types of data.
A Structured Review of the Validity of BLEU
Authors: Ehud Reiter
Date: 2018-09-01 00:00:00
Proceeding: CL 2018 9
Tasks: Diagnostic, Machine Translation, Text Generation, Translation, Two-sample testing, valid

The BLEU metric has been widely used in NLP for over 15 years to evaluate NLP systems, especially in machine translation and natural language generation. I present a structured review of the evidence on whether BLEU is a valid evaluation technique{---}in other words, whether BLEU scores correlate with real-world utility and user-satisfaction of NLP systems; this review covers 284 correlations reported in 34 papers. Overall, the evidence supports using BLEU for diagnostic evaluation of MT systems (which is what it was originally proposed for), but does not support using BLEU outside of MT, for evaluation of individual texts, or for scientific hypothesis testing.
Native Language Identification With Classifier Stacking and Ensembles
Authors: Shervin Malmasi, Mark Dras
Date: 2018-09-01 00:00:00
Proceeding: CL 2018 9
Tasks: Cross-corpus, General Classification, Language Acquisition, Language Identification, Native Language Identification, Text Classification

Ensemble methods using multiple classifiers have proven to be among the most successful approaches for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of experiments using three ensemble-based models, testing each with multiple configurations and algorithms. This includes a rigorous application of meta-classification models for NLI, achieving state-of-the-art results on several large data sets, evaluated in both intra-corpus and cross-corpus modes.