pwc-archive/papers-with-abstracts huggingface dataset:
A Stylistic Analysis of Honest Deception: The Case of Seinfeld TV Series Sitcom
Authors: Mohcine El Baroudi
Date: 2021-04-17 00:00:00

Language is a powerful tool if used in the correct manner. It is the major mode of communication, and using the correct choice of words and styles can serve to have a long-lasting impact. Stylistics is the study of the use of various language styles in communication to pass a message with a bigger impact or to communicate indirectly. Stylistic analysis, therefore, is the study of the use of linguistic styles in texts to determine how a style has been used, what is communicated and how it is communicated. Honest deception is the use of a choice of words to imply something different from the literal meaning. A person listening or reading a text where honest deception has been used and with a literal understanding may completely miss out on the point. This is because the issue of honesty and falsehood arises. However, it would be better to understand that honest deception is used with the intention of having a lasting impact rather than to deceive the readers, viewers or listeners. The major styles used in honest deception are hyperboles, litotes, irony and sarcasm. The Seinfeld Sitcom TV series was a situational TV comedy show aired from 1990 to 1998. the show attempts to bring to the understanding the daily life of a comedian and how comedian views life experiences and convert them into hilarious jokes. It also shows Jerry's struggle with getting the right partner from the many women who come into his life. Reflecting on honest deception in the Seinfeld sitcom TV series, this paper is going to investigate how honest deception has been used in the series, why it has been used and what is being communicated. The study is going to use a recapitulative form to give a better analysis and grouping of the different styles used in honest deception throughout the series.
XLEnt: Mining a Large Cross-lingual Entity Dataset with Lexical-Semantic-Phonetic Word Alignment
Authors: Ahmed El-Kishky, Adithya Renduchintala, James Cross, Francisco Guzmán, Philipp Koehn
Date: 2021-04-17 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Machine Translation, Multilingual NLP, Translation, Word Alignment

Cross-lingual named-entity lexica are an important resource to multilingual NLP tasks such as machine translation and cross-lingual wikification. While knowledge bases contain a large number of entities in high-resource languages such as English and French, corresponding entities for lower-resource languages are often missing. To address this, we propose Lexical-Semantic-Phonetic Align (LSP-Align), a technique to automatically mine cross-lingual entity lexica from mined web data. We demonstrate LSP-Align outperforms baselines at extracting cross-lingual entity pairs and mine 164 million entity pairs from 120 different languages aligned with English. We release these cross-lingual entity pairs along with the massively multilingual tagged named entity corpus as a resource to the NLP community.
Sentence Alignment with Parallel Documents Facilitates Biomedical Machine Translation
Authors: Shengxuan Luo, Huaiyuan Ying, Jiao Li, Sheng Yu
Date: 2021-04-17 00:00:00
Tasks: Articles, Machine Translation, NMT, Sentence, Translation, Word Embeddings, Word Similarity

Objective: Today's neural machine translation (NMT) can achieve near human-level translation quality and greatly facilitates international communications, but the lack of parallel corpora poses a key problem to the development of translation systems for highly specialized domains, such as biomedicine. This work presents an unsupervised algorithm for deriving parallel corpora from document-level translations by using sentence alignment and explores how training materials affect the performance of biomedical NMT systems. Materials and Methods: Document-level translations are mixed to train bilingual word embeddings (BWEs) for the evaluation of cross-lingual word similarity, and sentence distance is defined by combining semantic and positional similarities of the sentences. The alignment of sentences is formulated as an extended earth mover's distance problem. A Chinese-English biomedical parallel corpus is derived with the proposed algorithm using bilingual articles from UpToDate and translations of PubMed abstracts, which is then used for the training and evaluation of NMT. Results: On two manually aligned translation datasets, the proposed algorithm achieved accurate sentence alignment in the 1-to-1 cases and outperformed competing algorithms in the many-to-many cases. The NMT model fine-tuned on biomedical data significantly improved the in-domain translation quality (zh-en: +17.72 BLEU; en-zh: +17.02 BLEU). Both the size of the training data and the combination of different corpora can significantly affect the model's performance. Conclusion: The proposed algorithm relaxes the assumption for sentence alignment and effectively generates accurate translation pairs that facilitate training high quality biomedical NMT models.
Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems
Authors: Evgeniia Razumovskaia, Goran Glavaš, Olga Majewska, Edoardo M. Ponti, Anna Korhonen, Ivan Vulić
Date: 2021-04-17 00:00:00
Tasks: Cross-Lingual Transfer, Machine Translation, Task-Oriented Dialogue Systems, Text Generation

In task-oriented dialogue (ToD), a user holds a conversation with an artificial agent to complete a concrete task. Although this technology represents one of the central objectives of AI and has been the focus of ever more intense research and development efforts, it is currently limited to a few narrow domains (e.g., food ordering, ticket booking) and a handful of languages (e.g., English, Chinese). This work provides an extensive overview of existing methods and resources in multilingual ToD as an entry point to this exciting and emerging field. We find that the most critical factor preventing the creation of truly multilingual ToD systems is the lack of datasets in most languages for both training and evaluation. In fact, acquiring annotations or human feedback for each component of modular systems or for data-hungry end-to-end systems is expensive and tedious. Hence, state-of-the-art approaches to multilingual ToD mostly rely on (zero- or few-shot) cross-lingual transfer from resource-rich languages (almost exclusively English), either by means of machine translation or multilingual representations. These approaches are currently viable only for typologically similar languages and languages with parallel / monolingual corpora available. On the other hand, their effectiveness beyond these boundaries is doubtful or hard to assess due to the lack of linguistically diverse benchmarks (especially for natural language generation and end-to-end evaluation). To overcome this limitation, we draw parallels between components of the ToD pipeline and other NLP tasks, which can inspire solutions for learning in low-resource scenarios. Finally, we list additional challenges that multilinguality poses for related areas (such as speech and human-centred evaluation), and indicate future directions that hold promise to further expand language coverage and dialogue capabilities of current ToD systems.
DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages
Authors: Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, Barbara McGillivray
Date: 2021-04-17 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Clustering, Knowledge Graphs

Word meaning is notoriously difficult to capture, both synchronically and diachronically. In this paper, we describe the creation of the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based on 100,000 human semantic proximity judgments. We thoroughly describe the multi-round incremental annotation process, the choice for a clustering algorithm to group usages into senses, and possible - diachronic and synchronic - uses for this dataset.
Multi-Perspective Abstractive Answer Summarization
Authors: Alexander R. Fabbri, Xiaojian Wu, Srini Iyer, Mona Diab
Date: 2021-04-17 00:00:00
Tasks: Community Question Answering, Question Answering, Sentence

Community Question Answering (CQA) forums such as Stack Overflow and Yahoo! Answers contain a rich resource of answers to a wide range of questions. Each question thread can receive a large number of answers with different perspectives. The goal of multi-perspective answer summarization is to produce a summary that includes all perspectives of the answer. A major obstacle for multi-perspective, abstractive answer summarization is the absence of a dataset to provide supervision for producing such summaries. This work introduces a novel dataset creation method to automatically create multi-perspective, bullet-point abstractive summaries from an existing CQA forum. Supervision provided by this dataset trains models to inherently produce multi-perspective summaries. Additionally, to train models to output more diverse, faithful answer summaries while retaining multiple perspectives, we propose a multi-reward optimization technique coupled with a sentence-relevance prediction multi-task loss. Our methods demonstrate improved coverage of perspectives and faithfulness as measured by automatic and human evaluations compared to a strong baseline.
The challenges of temporal alignment on Twitter during crises
Authors: Aniket Pramanick, Tilman Beck, Kevin Stowe, Iryna Gurevych
Date: 2021-04-17 00:00:00
Tasks: Domain Adaptation

Language use changes over time, and this impacts the effectiveness of NLP systems. This phenomenon is even more prevalent in social media data during crisis events where meaning and frequency of word usage may change over the course of days. Contextual language models fail to adapt temporally, emphasizing the need for temporal adaptation in models which need to be deployed over an extended period of time. While existing approaches consider data spanning large periods of time (from years to decades), shorter time spans are critical for crisis data. We quantify temporal degradation for this scenario and propose methods to cope with performance loss by leveraging techniques from domain adaptation. To the best of our knowledge, this is the first effort to explore effects of rapid language change driven by adversarial adaptations, particularly during natural and human-induced disasters. Through extensive experimentation on diverse crisis datasets, we analyze under what conditions our approaches outperform strong baselines while highlighting the current limitations of temporal adaptation methods in scenarios where access to unlabeled data is scarce.
The Topic Confusion Task: A Novel Scenario for Authorship Attribution
Authors: Malik H. Altakrori, Jackie Chi Kit Cheung, Benjamin C. M. Fung
Date: 2021-04-17 00:00:00
Tasks: Authorship Attribution

Authorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors. Researchers have investigated same-topic and cross-topic scenarios of authorship attribution, which differ according to whether new, unseen topics are used in the testing phase. However, neither scenario allows us to explain whether errors are caused by a failure to capture authorship writing style or by a topic shift. Motivated by this, we propose the \emph{topic confusion} task where we switch the author-topic configuration between the training and testing sets. This setup allows us to distinguish two types of errors: those caused by the topic shift and those caused by the features' inability to capture the writing styles. We show that stylometric features with part-of-speech tags are the least susceptible to topic variations. We further show that combining them with other features leads to significantly lower topic confusion and higher attribution accuracy. Finally, we show that pretrained language models such as BERT and RoBERTa perform poorly on this task and are surpassed by simple features such as word-level $n$-grams.
The Impact of ASR on the Automatic Analysis of Linguistic Complexity and Sophistication in Spontaneous L2 Speech
Authors: Yu Qiao, Wei Zhou, Elma Kerz, Ralf Schlüter
Date: 2021-04-17 00:00:00
Tasks: Benchmarking

In recent years, automated approaches to assessing linguistic complexity in second language (L2) writing have made significant progress in gauging learner performance, predicting human ratings of the quality of learner productions, and benchmarking L2 development. In contrast, there is comparatively little work in the area of speaking, particularly with respect to fully automated approaches to assessing L2 spontaneous speech. While the importance of a well-performing ASR system is widely recognized, little research has been conducted to investigate the impact of its performance on subsequent automatic text analysis. In this paper, we focus on this issue and examine the impact of using a state-of-the-art ASR system for subsequent automatic analysis of linguistic complexity in spontaneously produced L2 speech. A set of 30 selected measures were considered, falling into four categories: syntactic, lexical, n-gram frequency, and information-theoretic measures. The agreement between the scores for these measures obtained on the basis of ASR-generated vs. manual transcriptions was determined through correlation analysis. A more differential effect of ASR performance on specific types of complexity measures when controlling for task type effects is also presented.
Multilingual and Cross-Lingual Intent Detection from Spoken Data
Authors: Daniela Gerz, Pei-Hao Su, Razvan Kusztos, Avishek Mondal, Michał Lis, Eshan Singhal, Nikola Mrkšić, Tsung-Hsien Wen, Ivan Vulić
Date: 2021-04-17 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Few-Shot Learning, Intent Detection, Machine Translation, Sentence, speech-recognition, Speech Recognition, Translation

We present a systematic study on multilingual and cross-lingual intent detection from spoken data. The study leverages a new resource put forth in this work, termed MInDS-14, a first training and evaluation resource for the intent detection task with spoken data. It covers 14 intents extracted from a commercial system in the e-banking domain, associated with spoken examples in 14 diverse language varieties. Our key results indicate that combining machine translation models with state-of-the-art multilingual sentence encoders (e.g., LaBSE) can yield strong intent detectors in the majority of target languages covered in MInDS-14, and offer comparative analyses across different axes: e.g., zero-shot versus few-shot learning, translation direction, and impact of speech recognition. We see this work as an important step towards more inclusive development and evaluation of multilingual intent detectors from spoken data, in a much wider spectrum of languages compared to prior work.
Minimal Supervision for Morphological Inflection
Authors: Omer Goldman, Reut Tsarfaty
Date: 2021-04-17 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Morphological Inflection

Neural models for the various flavours of morphological inflection tasks have proven to be extremely accurate given ample labeled data -- data that may be slow and costly to obtain. In this work we aim to overcome this annotation bottleneck by bootstrapping labeled data from a seed as little as {\em five} labeled paradigms, accompanied by a large bulk of unlabeled text. Our approach exploits different kinds of regularities in morphological systems in a two-phased setup, where word tagging based on {\em analogies} is followed by word pairing based on {\em distances}. We experiment with the Paradigm Cell Filling Problem over eight typologically different languages, and find that, in languages with relatively simple morphology, orthographic regularities on their own allow inflection models to achieve respectable accuracy. Combined orthographic and semantic regularities alleviate difficulties with particularly complex morpho-phonological systems. Our results suggest that hand-crafting many tagged examples might be an unnecessary effort. However, more work is needed in order to address rarely used forms.
Revisiting Few-shot Relation Classification: Evaluation Data and Classification Schemes
Authors: Ofer Sabo, Yanai Elazar, Yoav Goldberg, Ido Dagan
Date: 2021-04-17 00:00:00
Tasks: Classification, Few-Shot Learning, Few-Shot Relation Classification, General Classification, Relation, Relation Classification

We explore Few-Shot Learning (FSL) for Relation Classification (RC). Focusing on the realistic scenario of FSL, in which a test instance might not belong to any of the target categories (none-of-the-above, aka NOTA), we first revisit the recent popular dataset structure for FSL, pointing out its unrealistic data distribution. To remedy this, we propose a novel methodology for deriving more realistic few-shot test data from available datasets for supervised RC, and apply it to the TACRED dataset. This yields a new challenging benchmark for FSL RC, on which state of the art models show poor performance. Next, we analyze classification schemes within the popular embedding-based nearest-neighbor approach for FSL, with respect to constraints they impose on the embedding space. Triggered by this analysis we propose a novel classification scheme, in which the NOTA category is represented as learned vectors, shown empirically to be an appealing option for FSL.
Learning to Share by Masking the Non-shared for Multi-domain Sentiment Classification
Authors: Jianhua Yuan, Yanyan Zhao, Bing Qin, Ting Liu
Date: 2021-04-17 00:00:00
Tasks: General Classification, Multi-Domain Sentiment Classification, Sentence, Sentiment Analysis, Sentiment Classification

Multi-domain sentiment classification deals with the scenario where labeled data exists for multiple domains but insufficient for training effective sentiment classifiers that work across domains. Thus, fully exploiting sentiment knowledge shared across domains is crucial for real world applications. While many existing works try to extract domain-invariant features in high-dimensional space, such models fail to explicitly distinguish between shared and private features at text-level, which to some extent lacks interpretablity. Based on the assumption that removing domain-related tokens from texts would help improve their domain-invariance, we instead first transform original sentences to be domain-agnostic. To this end, we propose the BertMasker network which explicitly masks domain-related words from texts, learns domain-invariant sentiment features from these domain-agnostic texts, and uses those masked words to form domain-aware sentence representations. Empirical experiments on a well-adopted multiple domain sentiment classification dataset demonstrate the effectiveness of our proposed model on both multi-domain sentiment classification and cross-domain settings, by increasing the accuracy by 0.94% and 1.8% respectively. Further analysis on masking proves that removing those domain-related and sentiment irrelevant tokens decreases texts' domain distinction, resulting in the performance degradation of a BERT-based domain classifier by over 12%.
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation
Authors: Seiichiro Kondo, Kengo Hotate, Masahiro Kaneko, Mamoru Komachi
Date: 2021-04-17 00:00:00
Proceeding: NAACL 2021 4
Tasks: Data Augmentation, Machine Translation, NMT, Sentence, Translation

Neural machine translation (NMT) has recently gained widespread attention because of its high translation accuracy. However, it shows poor performance in the translation of long sentences, which is a major issue in low-resource languages. It is assumed that this issue is caused by insufficient number of long sentences in the training data. Therefore, this study proposes a simple data augmentation method to handle long sentences. In this method, we use only the given parallel corpora as the training data and generate long sentences by concatenating two sentences. Based on the experimental results, we confirm improvements in long sentence translation by the proposed data augmentation method, despite its simplicity. Moreover, the translation quality is further improved by the proposed method, when combined with back-translation.
Frequency-based Distortions in Contextualized Word Embeddings
Authors: Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky
Date: 2021-04-17 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Embeddings

How does word frequency in pre-training data affect the behavior of similarity metrics in contextualized BERT embeddings? Are there systematic ways in which some word relationships are exaggerated or understated? In this work, we explore the geometric characteristics of contextualized word embeddings with two novel tools: (1) an identity probe that predicts the identity of a word using its embedding; (2) the minimal bounding sphere for a word's contextualized representations. Our results reveal that words of high and low frequency differ significantly with respect to their representational geometry. Such differences introduce distortions: when compared to human judgments, point estimates of embedding similarity (e.g., cosine similarity) can over- or under-estimate the semantic similarity of two words, depending on the frequency of those words in the training data. This has downstream societal implications: BERT-Base has more trouble differentiating between South American and African countries than North American and European ones. We find that these distortions persist when using BERT-Multilingual, suggesting that they cannot be easily fixed with additional data, which in turn introduces new distortions.
A multilabel approach to morphosyntactic probing
Authors: Naomi Tachikawa Shapiro, Amandalynne Paullada, Shane Steinert-Threlkeld
Date: 2021-04-17 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Word Embeddings

We introduce a multilabel probing task to assess the morphosyntactic representations of word embeddings from multilingual language models. We demonstrate this task with multilingual BERT (Devlin et al., 2018), training probes for seven typologically diverse languages of varying morphological complexity: Afrikaans, Croatian, Finnish, Hebrew, Korean, Spanish, and Turkish. Through this simple but robust paradigm, we show that multilingual BERT renders many morphosyntactic features easily and simultaneously extractable (e.g., gender, grammatical case, pronominal type). We further evaluate the probes on six "held-out" languages in a zero-shot transfer setting: Arabic, Chinese, Marathi, Slovenian, Tagalog, and Yoruba. This style of probing has the added benefit of revealing the linguistic properties that language models recognize as being shared across languages. For instance, the probes performed well on recognizing nouns in the held-out languages, suggesting that multilingual BERT has a conception of noun-hood that transcends individual languages; yet, the same was not true of adjectives.
Syntactic structures and the general Markov models
Authors: Sitanshu Gakkhar, Matilde Marcolli
Date: 2021-04-17 00:00:00

We study phylogenetic signal present in syntactic information by considering the syntactic structures data from Longobardi (2017b), Collins (2010), Ceolin et al. (2020) and Koopman (2011). Focusing first on the general Markov models, we explore how well the the syntactic structures data conform to the hypothesis required by these models. We do this by comparing derived phylogenetic trees against trees agreed on by the linguistics community. We then interpret the methods of Ceolin et al. (2020) as an infinite sites evolutionary model and compare the consistency of the data with this alternative. The ideas and methods discussed in the present paper are more generally applicable than to the specific setting of syntactic structures, and can be used in other contexts, when analyzing consistency of data with against hypothesized evolutionary models.