pwc-archive/papers-with-abstracts huggingface dataset:
Dynamics of an idiostyle of a Russian suicidal blogger
Authors: Tatiana Litvinova, Olga Litvinova, Pavel Seredin
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Data Visualization, Text Classification

Over 800000 people die of suicide each year. It is es-timated that by the year 2020, this figure will have in-creased to 1.5 million. It is considered to be one of the major causes of mortality during adolescence. Thus there is a growing need for methods of identifying su-icidal individuals. Language analysis is known to be a valuable psychodiagnostic tool, however the material for such an analysis is not easy to obtain. Currently as the Internet communications are developing, there is an opportunity to study texts of suicidal individuals. Such an analysis can provide a useful insight into the peculiarities of suicidal thinking, which can be used to further develop methods for diagnosing the risk of suicidal behavior. The paper analyzes the dynamics of a number of linguistic parameters of an idiostyle of a Russian-language blogger who died by suicide. For the first time such an analysis has been conducted using the material of Russian online texts. For text processing, the LIWC program is used. A correlation analysis was performed to identify the relationship between LIWC variables and number of days prior to suicide. Data visualization, as well as comparison with the results of related studies was performed.
Anaphora Resolution for Twitter Conversations: An Exploratory Study
Authors: Berfin Akta{\c{s}}, Tatjana Scheffler, Manfred Stede
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6

We present a corpus study of pronominal anaphora on Twitter conversations. After outlining the specific features of this genre, with respect to reference resolution, we explain the construction of our corpus and the annotation steps. From this we derive a list of phenomena that need to be considered when performing anaphora resolution on this type of data. Finally, we test the performance of an off-the-shelf resolution system, and provide some qualitative error analysis.
Anaphora Resolution with the ARRAU Corpus
Authors: Massimo Poesio, Yulia Grishina, Varada Kolhatkar, Nafise Moosavi, Ina Roesiger, Adam Roussel, Fabian Simonjetz, Alex Uma, ra, Olga Uryupina, Juntao Yu, Heike Zinsmeister
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6

The ARRAU corpus is an anaphorically annotated corpus of English providing rich linguistic information about anaphora resolution. The most distinctive feature of the corpus is the annotation of a wide range of anaphoric relations, including bridging references and discourse deixis in addition to identity (coreference). Other distinctive features include treating all NPs as markables, including non-referring NPs; and the annotation of a variety of morphosyntactic and semantic mention and entity attributes, including the genericity status of the entities referred to by markables. The corpus however has not been extensively used for anaphora resolution research so far. In this paper, we discuss three datasets extracted from the ARRAU corpus to support the three subtasks of the CRAC 2018 Shared Task{--}identity anaphora resolution over ARRAU-style markables, bridging references resolution, and discourse deixis; the evaluation scripts assessing system performance on those datasets; and preliminary results on these three tasks that may serve as baseline for subsequent research in these phenomena.
Rule- and Learning-based Methods for Bridging Resolution in the ARRAU Corpus
Authors: Ina Roesiger
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Bridging Anaphora Resolution

We present two systems for bridging resolution, which we submitted to the CRAC shared task on bridging anaphora resolution in the ARRAU corpus (track 2): a rule-based approach following Hou et al. 2014 and a learning-based approach. The re-implementation of Hou et al. 2014 achieves very poor performance when being applied to ARRAU. We found that the reasons for this lie in the different bridging annotations: whereas the rule-based system suggests many referential bridging pairs, ARRAU contains mostly lexical bridging. We describe the differences between these two types of bridging and adapt the rule-based approach to be able to handle lexical bridging. The modified rule-based approach achieves reasonable performance on all (sub)-tasks and outperforms a simple learning-based approach.
Integrating Predictions from Neural-Network Relation Classifiers into Coreference and Bridging Resolution
Authors: Ina Roesiger, Maximilian K{\"o}per, Kim Anh Nguyen, Sabine Schulte im Walde
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Coreference Resolution, Relation

Cases of coreference and bridging resolution often require knowledge about semantic relations between anaphors and antecedents. We suggest state-of-the-art neural-network classifiers trained on relation benchmarks to predict and integrate likelihoods for relations. Two experiments with representations differing in noise and complexity improve our bridging but not our coreference resolver.
Towards Bridging Resolution in German: Data Analysis and Rule-based Experiments
Authors: Janis Pagel, Ina Roesiger
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Re-Ranking

Bridging resolution is the task of recognising bridging anaphors and linking them to their antecedents. While there is some work on bridging resolution for English, there is only little work for German. We present two datasets which contain bridging annotations, namely DIRNDL and GRAIN, and compare the performance of a rule-based system with a simple baseline approach on these two corpora. The performance for full bridging resolution ranges between an F1 score of 13.6{\%} for DIRNDL and 11.8{\%} for GRAIN. An analysis using oracle lists suggests that the system could, to a certain extent, benefit from ranking and re-ranking antecedent candidates. Furthermore, we investigate the importance of single features and show that the features used in our work seem promising for future bridging resolution approaches.
Detecting and Resolving Shell Nouns in German
Authors: Adam Roussel
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Question Answering, Sentiment Analysis

This paper describes the design and evaluation of a system for the automatic detection and resolution of shell nouns in German. Shell nouns are general nouns, such as fact, question, or problem, whose full interpretation relies on a content phrase located elsewhere in a text, which these nouns simultaneously serve to characterize and encapsulate. To accomplish this, the system uses a series of lexico-syntactic patterns in order to extract shell noun candidates and their content in parallel. Each pattern has its own classifier, which makes the final decision as to whether or not a link is to be established and the shell noun resolved. Overall, about 26.2{\%} of the annotated shell noun instances were correctly identified by the system, and of these cases, about 72.5{\%} are assigned the correct content phrase. Though it remains difficult to identify shell noun instances reliably (recall is accordingly low in this regard), this system usually assigns the right content to correctly classified cases. cases.
PAWS: A Multi-lingual Parallel Treebank with Anaphoric Relations
Authors: Anna Nedoluzhko, Michal Nov{\'a}k, Maciej Ogrodniczuk
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Coreference Resolution, Machine Translation

We present PAWS, a multi-lingual parallel treebank with coreference annotation. It consists of English texts from the Wall Street Journal translated into Czech, Russian and Polish. In addition, the texts are syntactically parsed and word-aligned. PAWS is based on PCEDT 2.0 and continues the tradition of multilingual treebanks with coreference annotation. The paper focuses on the coreference annotation in PAWS and its language-specific differences. PAWS offers linguistic material that can be further leveraged in cross-lingual studies, especially on coreference.
A Fine-grained Large-scale Analysis of Coreference Projection
Authors: Michal Nov{\'a}k
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Opinion Mining, Part-Of-Speech Tagging, Semantic Role Labeling

We perform a fine-grained large-scale analysis of coreference projection. By projecting gold coreference from Czech to English and vice versa on Prague Czech-English Dependency Treebank 2.0 Coref, we set an upper bound of a proposed projection approach for these two languages. We undertake a detailed thorough analysis that combines the analysis of projection{'}s subtasks with analysis of performance on individual mention types. The findings are accompanied with examples from the corpus.
Modeling Brain Activity Associated with Pronoun Resolution in English and Chinese
Authors: Jixing Li, Murielle Fabre, Wen-Ming Luh, John Hale
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6

Typological differences between English and Chinese suggest stronger reliance on salience of the antecedent during pronoun resolution in Chinese. We examined this hypothesis by correlating a difficulty measure of pronoun resolution derived by the activation-based ACT-R model with the brain activity of English and Chinese participants listening to a same audiobook during fMRI recording. The ACT-R model predicts higher overall difficulty for English speakers, which is supported at the brain level in left Broca{'}s area. More generally, it confirms that computational modeling approach is able to dissociate different dimensions that are involved in the complex process of pronoun resolution in the brain.
Event versus entity co-reference: Effects of context and form of referring expression
Authors: Sharid Lo{\'a}iciga, Luca Bevacqua, Hannah Rohde, Christian Hardmeier
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Form, Referring Expression, Sentence, Story Continuation

Anaphora resolution systems require both an enumeration of possible candidate antecedents and an identification process of the antecedent. This paper focuses on (i) the impact of the form of referring expression on entity-vs-event preferences and (ii) how properties of the passage interact with referential form. Two crowd-sourced story-continuation experiments were conducted, using constructed and naturally-occurring passages, to see how participants interpret \textit{It} and \textit{This} pronouns following a context sentence that makes available event and entity referents. Our participants show a strong, but not categorical, bias to use \textit{This} to refer to events and \textit{It} to refer to entities. However, these preferences vary with passage characteristics such as verb class (a proxy in our constructed examples for the number of explicit and implicit entities) and more subtle author intentions regarding subsequent re-mention (the original event-vs-entity re-mention of our corpus items).
On the Utility of Lay Summaries and AI Safety Disclosures: Toward Robust, Open Research Oversight
Authors: Allen Schmaltz
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Machine Translation

In this position paper, we propose that the community consider encouraging researchers to include two riders, a {``}Lay Summary{''} and an {``}AI Safety Disclosure{''}, as part of future NLP papers published in ACL forums that present user-facing systems. The goal is to encourage researchers{--}via a relatively non-intrusive mechanism{--}to consider the societal implications of technologies carrying (un)known and/or (un)knowable long-term risks, to highlight failure cases, and to provide a mechanism by which the general public (and scientists in other disciplines) can more readily engage in the discussion in an informed manner. This simple proposal requires minimal additional up-front costs for researchers; the lay summary, at least, has significant precedence in the medical literature and other areas of science; and the proposal is aimed to supplement, rather than replace, existing approaches for encouraging researchers to consider the ethical implications of their work, such as those of the Collaborative Institutional Training Initiative (CITI) Program and institutional review boards (IRBs).
Challenges in Finding Metaphorical Connections
Authors: Katy Gero, Lydia Chilton
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6

Poetry is known for its novel expression using figurative language. We introduce a writing task that contains the essential challenges of generating meaningful figurative language and can be evaluated. We investigate how to find metaphorical connections between abstract themes and concrete domains by asking people to write four-line poems on a given metaphor, such as {``}death is a rose{''} or {``}anger is wood{''}. We find that only 21{\%} of poems successfully make a metaphorical connection. We present five alternate ways people respond to the prompt and release our dataset of 100 categorized poems. We suggest opportunities for computational approaches.
Linguistic Features of Sarcasm and Metaphor Production Quality
Authors: Stephen Skalicky, Scott Crossley
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Language Identification

Using linguistic features to detect figurative language has provided a deeper in-sight into figurative language. The purpose of this study is to assess whether linguistic features can help explain differences in quality of figurative language. In this study a large corpus of metaphors and sarcastic responses are collected from human subjects and rated for figurative language quality based on theoretical components of metaphor, sarcasm, and creativity. Using natural language processing tools, specific linguistic features related to lexical sophistication and semantic cohesion were used to predict the human ratings of figurative language quality. Results demonstrate linguistic features were able to predict small amounts of variance in metaphor and sarcasm production quality.
Leveraging Syntactic Constructions for Metaphor Identification
Authors: Kevin Stowe, Martha Palmer
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Natural Language Understanding

Identification of metaphoric language in text is critical for generating effective semantic representations for natural language understanding. Computational approaches to metaphor identification have largely relied on heuristic based models or feature-based machine learning, using hand-crafted lexical resources coupled with basic syntactic information. However, recent work has shown the predictive power of syntactic constructions in determining metaphoric source and target domains (Sullivan 2013). Our work intends to explore syntactic constructions and their relation to metaphoric language. We undertake a corpus-based analysis of predicate-argument constructions and their metaphoric properties, and attempt to effectively represent syntactic constructions as features for metaphor processing, both in identifying source and target domains and in distinguishing metaphoric words from non-metaphoric.
Literal, Metphorical or Both? Detecting Metaphoricity in Isolated Adjective-Noun Phrases
Authors: Agnieszka Mykowiecka, Malgorzata Marciniak, Aleks Wawer, er
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: General Classification, Machine Translation, Natural Language Inference, Word Embeddings

The paper addresses the classification of isolated Polish adjective-noun phrases according to their metaphoricity. We tested neural networks to predict if a phrase has a literal or metaphorical sense or can have both senses depending on usage. The input to the neural network consists of word embeddings, but we also tested the impact of information about the domain of the adjective and about the abstractness of the noun. We applied our solution to English data available on the Internet and compared it to results published in papers. We found that the solution based on word embeddings only can achieve results comparable with complex solutions requiring additional information.
Catching Idiomatic Expressions in EFL Essays
Authors: Michael Flor, Beata Beigman Klebanov
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6

This paper presents an exploratory study on large-scale detection of idiomatic expressions in essays written by non-native speakers of English. We describe a computational search procedure for automatic detection of idiom-candidate phrases in essay texts. The study used a corpus of essays written during a standardized examination of English language proficiency. Automatically-flagged candidate expressions were manually annotated for idiomaticity. The study found that idioms are widely used in EFL essays. The study also showed that a search algorithm that accommodates the syntactic and lexical exibility of idioms can increase the recall of idiom instances by 30{\%}, but it also increases the amount of false positives.
Predicting Human Metaphor Paraphrase Judgments with Deep Neural Networks
Authors: Yuri Bizzoni, Shalom Lappin
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Binary Classification, Classification, General Classification, Natural Language Inference, Semantic Textual Similarity, Sentence

We propose a new annotated corpus for metaphor interpretation by paraphrase, and a novel DNN model for performing this task. Our corpus consists of 200 sets of 5 sentences, with each set containing one reference metaphorical sentence, and four ranked candidate paraphrases. Our model is trained for a binary classification of paraphrase candidates, and then used to predict graded paraphrase acceptability. It reaches an encouraging 75{\%} accuracy on the binary classification task, and high Pearson (.75) and Spearman (.68) correlations on the gradient judgment prediction task.
A Report on the 2018 VUA Metaphor Detection Shared Task
Authors: Chee Wee (Ben) Leong, Beata Beigman Klebanov, Ekaterina Shutova
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Benchmarking

As the community working on computational approaches to figurative language is growing and as methods and data become increasingly diverse, it is important to create widely shared empirical knowledge of the level of system performance in a range of contexts, thus facilitating progress in this area. One way of creating such shared knowledge is through benchmarking multiple systems on a common dataset. We report on the shared task on metaphor identification on the VU Amsterdam Metaphor Corpus conducted at the NAACL 2018 Workshop on Figurative Language Processing.
An LSTM-CRF Based Approach to Token-Level Metaphor Detection
Authors: Malay Pramanick, Ashim Gupta, Pabitra Mitra
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Machine Translation

Automatic processing of figurative languages is gaining popularity in NLP community for their ubiquitous nature and increasing volume. In this era of web 2.0, automatic analysis of sarcasm and metaphors is important for their extensive usage. Metaphors are a part of figurative language that compares different concepts, often on a cognitive level. Many approaches have been proposed for automatic detection of metaphors, even using sequential models or neural networks. In this paper, we propose a method for detection of metaphors at the token level using a hybrid model of Bidirectional-LSTM and CRF. We used fewer features, as compared to the previous state-of-the-art sequential model. On experimentation with VUAMC, our method obtained an F-score of 0.674.
Unsupervised Detection of Metaphorical Adjective-Noun Pairs
Authors: Malay Pramanick, Pabitra Mitra
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Clustering, Machine Translation, Word Sense Disambiguation

Metaphor is a popular figure of speech. Popularity of metaphors calls for their automatic identification and interpretation. Most of the unsupervised methods directed at detection of metaphors use some hand-coded knowledge. We propose an unsupervised framework for metaphor detection that does not require any hand-coded knowledge. We applied clustering on features derived from Adjective-Noun pairs for classifying them into two disjoint classes. We experimented with adjective-noun pairs of a popular dataset annotated for metaphors and obtained an accuracy of 72.87{\%} with k-means clustering algorithm.
Phrase-Level Metaphor Identification Using Distributed Representations of Word Meaning
Authors: Omnia Zayed, John Philip McCrae, Paul Buitelaar
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Machine Translation, Semantic Textual Similarity, Text Simplification, Text Summarization, Word Embeddings

Metaphor is an essential element of human cognition which is often used to express ideas and emotions that might be difficult to express using literal language. Processing metaphoric language is a challenging task for a wide range of applications ranging from text simplification to psychotherapy. Despite the variety of approaches that are trying to process metaphor, there is still a need for better models that mimic the human cognition while exploiting fewer resources. In this paper, we present an approach based on distributional semantics to identify metaphors on the phrase-level. We investigated the use of different word embeddings models to identify verb-noun pairs where the verb is used metaphorically. Several experiments are conducted to show the performance of the proposed approach on benchmark datasets.
Bigrams and BiLSTMs Two Neural Networks for Sequential Metaphor Detection
Authors: Yuri Bizzoni, Mehdi Ghanimifard
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Vocal Bursts Valence Prediction, Word Embeddings

We present and compare two alternative deep neural architectures to perform word-level metaphor detection on text: a bi-LSTM model and a new structure based on recursive feed-forward concatenation of the input. We discuss different versions of such models and the effect that input manipulation - specifically, reducing the length of sentences and introducing concreteness scores for words - have on their performance.
Computationally Constructed Concepts: A Machine Learning Approach to Metaphor Interpretation Using Usage-Based Construction Grammatical Cues
Authors: Zachary Rosen
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Attribute, BIG-bench Machine Learning, Deep Learning, General Classification, Word Embeddings

The current study seeks to implement a deep learning classification algorithm using argument-structure level representation of metaphoric constructions, for the identification of source domain mappings in metaphoric utterances. It thus builds on previous work in computational metaphor interpretation (Mohler et al. 2014; Shutova 2010; Bollegala {\&} Shutova 2013; Hong 2016; Su et al. 2017) while implementing a theoretical framework based off of work in the interface of metaphor and construction grammar (Sullivan 2006, 2007, 2013). The results indicate that it is possible to achieve an accuracy of approximately 80.4{\%} using the proposed method, combining construction grammatical features with a simple deep learning NN. I attribute this increase in accuracy to the use of constructional cues, extracted from the raw text of metaphoric instances.
Neural Metaphor Detecting with CNN-LSTM Model
Authors: Chuhan Wu, Fangzhao Wu, Yubo Chen, Sixing Wu, Zhigang Yuan, Yongfeng Huang
Date: 2018-06-01 00:00:00
Proceeding: WS 2018 6
Tasks: Machine Translation, model, POS, Sentiment Analysis

Metaphors are figurative languages widely used in daily life and literatures. It{'}s an important task to detect the metaphors evoked by texts. Thus, the metaphor shared task is aimed to extract metaphors from plain texts at word level. We propose to use a CNN-LSTM model for this task. Our model combines CNN and LSTM layers to utilize both local and long-range contextual information for identifying metaphorical information. In addition, we compare the performance of the softmax classifier and conditional random field (CRF) for sequential labeling in this task. We also incorporated some additional features such as part of speech (POS) tags and word cluster to improve the performance of model. Our best model achieved 65.06{\%} F-score in the all POS testing subtask and 67.15{\%} in the verbs testing subtask.