pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Adverbial Identification in Modern Chinese Literature
Authors: Wenxiu Xie, John Lee, Fangqiong Zhan, Xiao Han, Chi-Yin Chow
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 11
Tasks: Language Modeling, Language Modelling

In many languages, adverbials can be derived from words of various parts-of-speech. In Chinese, the derivation may be marked either with the standard adverbial marker DI, or the non-standard marker DE. Since DE also serves double duty as the attributive marker, accurate identification of adverbials requires disambiguation of its syntactic role. As parsers are trained predominantly on texts using the standard adverbial marker DI, they often fail to recognize adverbials suffixed with the non-standard DE. This paper addresses this problem with an unsupervised, rule-based approach for adverbial identification that utilizes dependency tree patterns. Experiment results show that this approach outperforms a masked language model baseline. We apply this approach to analyze standard and non-standard adverbial marker usage in modern Chinese literature.
Data-Driven Detection of General Chiasmi Using Lexical and Semantic Features
Authors: Felix Schneider, Björn Barz, Phillip Brandes, Sophie Marshall, Joachim Denzler
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 11
Tasks: Argument Mining, Word Embeddings

Automatic detection of stylistic devices is an important tool for literary studies, e.g., for stylometric analysis or argument mining. A particularly striking device is the rhetorical figure called chiasmus, which involves the inversion of semantically or syntactically related words. Existing works focus on a special case of chiasmi that involve identical words in an A B B A pattern, so-called antimetaboles. In contrast, we propose an approach targeting the more general and challenging case A B B’ A’, where the words A, A’ and B, B’ constituting the chiasmus do not need to be identical but just related in meaning. To this end, we generalize the established candidate phrase mining strategy from antimetaboles to general chiasmi and propose novel features based on word embeddings and lemmata for capturing both semantic and syntactic information. These features serve as input for a logistic regression classifier, which learns to distinguish between rhetorical chiasmi and coincidental chiastic word orders without special meaning. We evaluate our approach on two datasets consisting of classical German dramas, four texts with annotated chiasmi and 500 unannotated texts. Compared to previous methods for chiasmus detection, our novel features improve the average precision from 17% to 28% and the precision among the top 100 results from 13% to 35%.
Translationese in Russian Literary Texts
Authors: Maria Kunilovskaya, Ekaterina Lapshinova-Koltunski, Ruslan Mitkov
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 11
Tasks: Specificity

The paper reports the results of a translationese study of literary texts based on translated and non-translated Russian. We aim to find out if translations deviate from non-translated literary texts, and if the established differences can be attributed to typological relations between source and target languages. We expect that literary translations from typologically distant languages should exhibit more translationese, and the fingerprints of individual source languages (and their families) are traceable in translations. We explore linguistic properties that distinguish non-translated Russian literature from translations into Russian. Our results show that non-translated fiction is different from translations to the degree that these two language varieties can be automatically classified. As expected, language typology is reflected in translations of literary texts. We identified features that point to linguistic specificity of Russian non-translated literature and to shining-through effects. Some of translationese features cut across all language pairs, while others are characteristic of literary translations from languages belonging to specific language families.
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese
Authors: Zuoyu Tian, Dylan Jarrett, Juan Escalona Torres, Patricia Amaral
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 11
Tasks: Outlier Detection, Word Embeddings

High quality distributional models can capture lexical and semantic relations between words. Hence, researchers design various intrinsic tasks to test whether such relations are captured. However, most of the intrinsic tasks are designed for modern languages, and there is a lack of evaluation methods for distributional models of historical corpora. In this paper, we conducted BAHP: a benchmark of assessing word embeddings in Historical Portuguese, which contains four types of tests: analogy, similarity, outlier detection, and coherence. We examined word2vec models generated from two historical Portuguese corpora in these four test sets. The results demonstrate that our test sets are capable of measuring the quality of vector space models and can provide a holistic view of the model’s ability to capture syntactic and semantic information. Furthermore, the methodology for the creation of our test sets can be easily extended to other historical languages.
The diffusion of scientific terms – tracing individuals’ influence in the history of science for English
Authors: Yuri Bizzoni, Stefania Degaetano-Ortlieb, Katrin Menzel, Elke Teich
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 11
Tasks: Astronomy

Tracing the influence of individuals or groups in social networks is an increasingly popular task in sociolinguistic studies. While methods to determine someone’s influence in shortterm contexts (e.g., social media, on-line political debates) are widespread, influence in longterm contexts is less investigated and may be harder to capture. We study the diffusion of scientific terms in an English diachronic scientific corpus, applying Hawkes Processes to capture the role of individual scientists as “influencers” or “influencees” in the diffusion of new concepts. Our findings on two major scientific discoveries in chemistry and astronomy of the 18th century reveal that modelling both the introduction and diffusion of scientific terms in a historical corpus as Hawkes Processes allows detecting patterns of influence between authors on a long-term scale.
A Pilot Study for BERT Language Modelling and Morphological Analysis for Ancient and Medieval Greek
Authors: Pranaydeep Singh, Gorik Rutten, Els Lefever
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 11
Tasks: Language Modeling, Language Modelling, Morphological Analysis

This paper presents a pilot study to automatic linguistic preprocessing of Ancient and Byzantine Greek, and morphological analysis more specifically. To this end, a novel subword-based BERT language model was trained on the basis of a varied corpus of Modern, Ancient and Post-classical Greek texts. Consequently, the obtained BERT embeddings were incorporated to train a fine-grained Part-of-Speech tagger for Ancient and Byzantine Greek. In addition, a corpus of Greek Epigrams was manually annotated and the resulting gold standard was used to evaluate the performance of the morphological analyser on Byzantine Greek. The experimental results show very good perplexity scores (4.9) for the BERT language model and state-of-the-art performance for the fine-grained Part-of-Speech tagger for in-domain data (treebanks containing a mixture of Classical and Medieval Greek), as well as for the newly created Byzantine Greek gold standard data set. The language models and associated code are made available for use at https://github.com/pranaydeeps/Ancient-Greek-BERT
Zero-Shot Information Extraction to Enhance a Knowledge Graph Describing Silk Textiles
Authors: Thomas Schleider, Raphael Troncy
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 11
Tasks: Common Sense Reasoning, Knowledge Graphs, Zero-Shot Learning

The knowledge of the European silk textile production is a typical case for which the information collected is heterogeneous, spread across many museums and sparse since rarely complete. Knowledge Graphs for this cultural heritage domain, when being developed with appropriate ontologies and vocabularies, enable to integrate and reconcile this diverse information. However, many of these original museum records still have some metadata gaps. In this paper, we present a zero-shot learning approach that leverages the ConceptNet common sense knowledge graph to predict categorical metadata informing about the silk objects production. We compared the performance of our approach with traditional supervised deep learning-based methods that do require training data. We demonstrate promising and competitive performance for similar datasets and circumstances and the ability to predict sometimes more fine-grained information. Our results can be reproduced using the code and datasets published at https://github.com/silknow/ZSL-KG-silk.
‘Tecnologica cosa’: Modeling Storyteller Personalities in Boccaccio’s ‘Decameron’
Authors: A. Cooper, Maria Antoniak, Christopher De Sa, Marilyn Migiel, David Mimno
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 11

We explore Boccaccio’s Decameron to see how digital humanities tools can be used for tasks that have limited data in a language no longer in contemporary use: medieval Italian. We focus our analysis on the question: Do the different storytellers in the text exhibit distinct personalities? To answer this question, we curate and release a dataset based on the authoritative edition of the text. We use supervised classification methods to predict storytellers based on the stories they tell, confirming the difficulty of the task, and demonstrate that topic modeling can extract thematic storyteller “profiles.”
WMDecompose: A Framework for Leveraging the Interpretable Properties of Word Mover’s Distance in Sociocultural Analysis
Authors: Mikael Brunila, Jack LaViolette
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 11

Despite the increasing popularity of NLP in the humanities and social sciences, advances in model performance and complexity have been accompanied by concerns about interpretability and explanatory power for sociocultural analysis. One popular model that takes a middle road is Word Mover’s Distance (WMD). Ostensibly adapted for its interpretability, WMD has nonetheless been used and further developed in ways which frequently discard its most interpretable aspect: namely, the word-level distances required for translating a set of words into another set of words. To address this apparent gap, we introduce WMDecompose: a model and Python library that 1) decomposes document-level distances into their constituent word-level distances, and 2) subsequently clusters words to induce thematic elements, such that useful lexical information is retained and summarized for analysis. To illustrate its potential in a social scientific context, we apply it to a longitudinal social media corpus to explore the interrelationship between conspiracy theories and conservative American discourses. Finally, because of the full WMD model’s high time-complexity, we additionally suggest a method of sampling document pairs from large datasets in a reproducible way, with tight bounds that prevent extrapolation of unreliable results due to poor sampling practices.
Period Classification in Chinese Historical Texts
Authors: Zuoyu Tian, Sandra Kübler
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 11
Tasks: Classification

In this study, we study language change in Chinese Biji by using a classification task: classifying Ancient Chinese texts by time periods. Specifically, we focus on a unique genre in classical Chinese literature: Biji (literally “notebook” or “brush notes”), i.e., collections of anecdotes, quotations, etc., anything authors consider noteworthy, Biji span hundreds of years across many dynasties and conserve informal language in written form. For these reasons, they are regarded as a good resource for investigating language change in Chinese (Fang, 2010). In this paper, we create a new dataset of 108 Biji across four dynasties. Based on the dataset, we first introduce a time period classification task for Chinese. Then we investigate different feature representation methods for classification. The results show that models using contextualized embeddings perform best. An analysis of the top features chosen by the word n-gram model (after bleaching proper nouns) confirms that these features are informative and correspond to observations and assumptions made by historical linguists.
A Mixed-Methods Analysis of Western and Hong Kong–based Reporting on the 2019–2020 Protests
Authors: Arya D. McCarthy, James Scharf, Giovanna Maria Dora Dore
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 11
Tasks: Articles, Sentiment Analysis

We apply statistical techniques from natural language processing to Western and Hong Kong–based English language newspaper articles that discuss the 2019–2020 Hong Kong protests of the Anti-Extradition Law Amendment Bill Movement. Topic modeling detects central themes of the reporting and shows the differing agendas toward one country, two systems. Embedding-based usage shift (at the word level) and sentiment analysis (at the document level) both support that Hong Kong–based reporting is more negative and more emotionally charged. A two-way test shows that while July 1, 2019 is a turning point for media portrayal, the differences between western- and Hong Kong–based reporting did not magnify when the protests began; rather, they already existed. Taken together, these findings clarify how the portrayal of activism in Hong Kong evolved throughout the Movement.
Stylometric Literariness Classification: the Case of Stephen King
Authors: Andreas van Cranenburgh, Erik Ketzan
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (LaTeCHCLfL, CLFL, LaTeCH) 2021 11
Tasks: Binary Classification, Classification

This paper applies stylometry to quantify the literariness of 73 novels and novellas by American author Stephen King, chosen as an extraordinary case of a writer who has been dubbed both “high” and “low” in literariness in critical reception. We operationalize literariness using a measure of stylistic distance (Cosine Delta) based on the 1000 most frequent words in two bespoke comparison corpora used as proxies for literariness: one of popular genre fiction, another of National Book Award-winning authors. We report that a supervised model is highly effective in distinguishing the two categories, with 94.6% macro average in a binary classification. We define two subsets of texts by King—“high” and “low” literariness works as suggested by critics and ourselves—and find that a predictive model does identify King’s Dark Tower series and novels such as Dolores Claiborne as among his most “literary” texts, consistent with critical reception, which has also ascribed postmodern qualities to the Dark Tower novels. Our results demonstrate the efficacy of Cosine Delta-based stylometry in quantifying the literariness of texts, while also highlighting the methodological challenges of literariness, especially in the case of Stephen King. The code and data to reproduce our results are available at https://github.com/andreasvc/kinglit
How does discourse affect Spanish-Chinese Translation? A case study based on a Spanish-Chinese parallel corpus
Authors: Shuyuan Cao
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (CODI) 2020 11
Tasks: Translation

With their huge speaking populations in the world, Spanish and Chinese occupy important positions in linguistic studies. Since the two languages come from different language systems, the translation between Spanish and Chinese is complicated. A comparative study for the language pair can discover the discourse differences between Spanish and Chinese, and can benefit the Spanish-Chinese translation. In this work, based on a Spanish-Chinese parallel corpus annotated with discourse information, we compare the annotation results between the language pair and analyze how discourse affects Spanish-Chinese translation. The research results in our study can help human translators who work with the language pair.
Beyond Adjacency Pairs: Hierarchical Clustering of Long Sequences for Human-Machine Dialogues
Authors: Maitreyee Maitreyee
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (CODI) 2020 11
Tasks: Clustering, Dependency Parsing, feature selection, Word Embeddings

This work proposes a framework to predict sequences in dialogues, using turn based syntactic features and dialogue control functions. Syntactic features were extracted using dependency parsing, while dialogue control functions were manually labelled. These features were transformed using tf-idf and word embedding; feature selection was done using Principal Component Analysis (PCA). We ran experiments on six combinations of features to predict sequences with Hierarchical Agglomerative Clustering. An analysis of the clustering results indicate that using word embeddings and syntactic features, significantly improved the results.
Exploring Span Representations in Neural Coreference Resolution
Authors: Patrick Kahardipraja, Olena Vyshnevska, Sharid Loáiciga
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (CODI) 2020 11
Tasks: coreference-resolution, Coreference Resolution

In coreference resolution, span representations play a key role to predict coreference links accurately. We present a thorough examination of the span representation derived by applying BERT on coreference resolution (Joshi et al., 2019) using a probing model. Our results show that the span representation is able to encode a significant amount of coreference information. In addition, we find that the head-finding attention mechanism involved in creating the spans is crucial in encoding coreference knowledge. Last, our analysis shows that the span representation cannot capture non-local coreference as efficiently as local coreference.
Supporting Comedy Writers: Predicting Audience’s Response from Sketch Comedy and Crosstalk Scripts
Authors: Maolin Li
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (CODI) 2020 11
Tasks: text-classification, Text Classification

Sketch comedy and crosstalk are two popular types of comedy. They can relieve people’s stress and thus benefit their mental health, especially when performances and scripts are high-quality. However, writing a script is time-consuming and its quality is difficult to achieve. In order to minimise the time and effort needed for producing an excellent script, we explore ways of predicting the audience’s response from the comedy scripts. For this task, we present a corpus of annotated scripts from popular television entertainment programmes in recent years. Annotations include a) text classification labels, indicating which actor’s lines made the studio audience laugh; b) information extraction labels, i.e. the text spans that made the audience laughed immediately after the performers said them. The corpus will also be useful for dialogue systems and discourse analysis, since our annotations are based on entire scripts. In addition, we evaluate different baseline algorithms. Experimental results demonstrate that BERT models can achieve the best predictions among all the baseline methods. Furthermore, we conduct an error analysis and investigate predictions across scripts with different styles.
Exploring Coreference Features in Heterogeneous Data
Authors: Ekaterina Lapshinova-Koltunski, Kerstin Kunz
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (CODI) 2020 11
Tasks: coreference-resolution, Coreference Resolution, feature selection, Machine Translation, text-classification, Text Classification, Translation

The present paper focuses on variation phenomena in coreference chains. We address the hypothesis that the degree of structural variation between chain elements depends on language-specific constraints and preferences and, even more, on the communicative situation of language production. We define coreference features that also include reference to abstract entities and events. These features are inspired through several sources – cognitive parameters, pragmatic factors and typological status. We pay attention to the distributions of these features in a dataset containing English and German texts of spoken and written discourse mode, which can be classified into seven different registers. We apply text classification and feature selection to find out how these variational dimensions (language, mode and register) impact on coreference features. Knowledge on the variation under analysis is valuable for contrastive linguistics, translation studies and multilingual natural language processing (NLP), e.g. machine translation or cross-lingual coreference resolution.
Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing
Authors: René Knaebel, Manfred Stede
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (CODI) 2020 11
Tasks: Discourse Parsing, Word Embeddings

This paper studies a novel model that simplifies the disambiguation of connectives for explicit discourse relations. We use a neural approach that integrates contextualized word embeddings and predicts whether a connective candidate is part of a discourse relation or not. We study the influence of those context-specific embeddings. Further, we show the benefit of training the tasks of connective disambiguation and sense classification together at the same time. The success of our approach is supported by state-of-the-art results.
DSNDM: Deep Siamese Neural Discourse Model with Attention for Text Pairs Categorization and Ranking
Authors: Alexander Chernyavskiy, Dmitry Ilvovsky
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (CODI) 2020 11
Tasks: Question Answering

In this paper, the utility and advantages of the discourse analysis for text pairs categorization and ranking are investigated. We consider two tasks in which discourse structure seems useful and important: automatic verification of political statements, and ranking in question answering systems. We propose a neural network based approach to learn the match between pairs of discourse tree structures. To this end, the neural TreeLSTM model is modified to effectively encode discourse trees and DSNDM model based on it is suggested to analyze pairs of texts. In addition, the integration of the attention mechanism in the model is proposed. Moreover, different ranking approaches are investigated for the second task. In the paper, the comparison with state-of-the-art methods is given. Experiments illustrate that combination of neural networks and discourse structure in DSNDM is effective since it reaches top results in the assigned tasks. The evaluation also demonstrates that discourse analysis improves quality for the processing of longer texts.
Do sentence embeddings capture discourse properties of sentences from Scientific Abstracts ?
Authors: Laurine Huber, Chaker Memmadi, Mathilde Dargnat, Yannick Toussaint
Date: 2020-11-01 00:00:00
Proceeding: EMNLP (CODI) 2020 11
Tasks: Sentence, Sentence Embeddings

We introduce four tasks designed to determine which sentence encoders best capture discourse properties of sentences from scientific abstracts, namely coherence and cohesion between clauses of a sentence, and discourse relations within sentences. We show that even if contextual encoders such as BERT or SciBERT encodes the coherence in discourse units, they do not help to predict three discourse relations commonly used in scientific abstracts. We discuss what these results underline, namely that these discourse relations are based on particular phrasing that allow non-contextual encoders to perform well.