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d252816084 | Ensembling BERT models often significantly improves accuracy, but at the cost of significantly more computation and memory footprint. In this work, we propose Multi-CLS BERT, a novel ensembling method for CLS-based prediction tasks that is almost as efficient as a single BERT model. Multi-CLS BERT uses multiple CLS tokens with a parameterization and objective that encourages their diversity. Thus instead of fine-tuning each BERT model in an ensemble (and running them all at test time), we need only fine-tune our single Multi-CLS BERT model (and run the one model at test time, ensembling just the multiple final CLS embeddings). To test its effectiveness, we build Multi-CLS BERT on top of a state-of-the-art pretraining method for BERT (Aroca-Ouellette and Rudzicz, 2020). In experiments on GLUE and SuperGLUE we show that our Multi-CLS BERT reliably improves both overall accuracy and confidence estimation. When only 100 training samples are available in GLUE, the Multi-CLS BERT Base model can even outperform the corresponding BERT Large model. We analyze the behavior of our Multi-CLS BERT, showing that it has many of the same characteristics and behavior as a typical BERT 5-way ensemble, but with nearly 4-times less computation and memory. | Multi-CLS BERT: An Efficient Alternative to Traditional Ensembling |
d6305198 | In this paper, we describe a tool designed to generate semi-automatically the sortal constraints specific to a domain to be used in a natural language (NL) understanding system. This tool is evaluated using the Sll,I Gemini NL understanding system in tile ATIS domain.of work we put into the first domain application 1.In this paper, we describe tile results of using this semi-automatic tool to port the (',e, udlii NL system to the ATIS domahi, a (lomltin that (ienlini had ah'eady been ported to, arid for which it ]lad achiew~,d high perl'orluance ~ttld gi'al'l-illiatical coverage using hand-written sortal constraints. Chossing a known domain, rather than a new one, allowed us to compare tile performance of tile derived sorts to the hand-written ones, holding the domain, grammar, and lexicon constant. It also allowed us to evahlate the selni-~ultoma.tically obtained cown'age using the ewduation tools provided for the A'I?IS corpus. | A TOOL FOR COLLECTING DOMAIN DEPENDENT SORTAL CONSTRAINTS FROM CORPORA |
d247292373 | Knowledge of difficulty level of questions helps a teacher in several ways, such as estimating students' potential quickly by asking carefully selected questions and improving quality of examination by modifying trivial and hard questions. Can we extract such benefits of instance difficulty in Natural Language Processing? To this end, we conduct Instance-Level Difficulty Analysis of Evaluation data (ILDAE) in a largescale setup of 23 datasets and demonstrate its five novel applications: 1) conducting efficientyet-accurate evaluations with fewer instances saving computational cost and time, 2) improving quality of existing evaluation datasets by repairing erroneous and trivial instances, 3) selecting the best model based on application requirements, 4) analyzing dataset characteristics for guiding future data creation, 5) estimating Out-of-Domain performance reliably. Comprehensive experiments for these applications lead to several interesting results, such as evaluation using just 5% instances (selected via ILDAE) achieves as high as 0.93 Kendall correlation with evaluation using complete dataset and computing weighted accuracy using difficulty scores leads to 5.2% higher correlation with Out-of-Domain performance. We release the difficulty scores 1 and hope our work will encourage research in this important yet understudied field of leveraging instance difficulty in evaluations. | ILDAE: Instance-Level Difficulty Analysis of Evaluation Data |
d222290670 | Two main approaches for evaluating the quality of machine-generated rationales are: 1) using human rationales as a gold standard; and 2) automated metrics based on how rationales affect model behavior. An open question, however, is how human rationales fare with these automatic metrics. Analyzing a variety of datasets and models, we find that human rationales do not necessarily perform well on these metrics. To unpack this finding, we propose improved metrics to account for modeldependent baseline performance. We then propose two methods to further characterize rationale quality, one based on model retraining and one on using "fidelity curves" to reveal properties such as irrelevance and redundancy. Our work leads to actionable suggestions for evaluating and characterizing rationales. | Evaluating and Characterizing Human Rationales |
d250390942 | Pre-trained word embedding models are easily distributed and applied, as they alleviate users from the effort to train models themselves. With widely distributed models, it is important to ensure that they do not exhibit undesired behaviour, such as biases against population groups. For this purpose, we carry out an empirical study on evaluating the bias of 15 publicly available, pre-trained word embeddings model based on three training algorithms (GloVe, word2vec, and fastText) with regard to four bias metrics (WEAT, SEMBIAS, DIRECT BIAS, and ECT). The choice of word embedding models and bias metrics is motivated by a literature survey over 37 publications which quantified bias on pre-trained word embeddings. Our results indicate that fastText is the least biased model (in 8 out of 12 cases) and small vector lengths lead to a higher bias. | An Empirical Study on the Fairness of Pre-trained Word Embeddings |
d49297559 | This survey discusses how recent developments in multimodal processing facilitate conceptual grounding of language. We categorize the information flow in multimodal processing with respect to cognitive models of human information processing and analyze different methods for combining multimodal representations. Based on this methodological inventory, we discuss the benefit of multimodal grounding for a variety of language processing tasks and the challenges that arise. We particularly focus on multimodal grounding of verbs which play a crucial role for the compositional power of language.Title and Abstract in GermanMultimodale konzeptuelle Verankerung für die automatische Sprachverarbeitung DieserÜberblick erörtert, wie aktuelle Entwicklungen in der automatischen Verarbeitung multimodaler Inhalte die konzeptuelle Verankerung sprachlicher Inhalte erleichtern können. Die automatischen Methoden zur Verarbeitung multimodaler Inhalte werden zunächst hinsichtlich der zugrundeliegenden kognitiven Modelle menschlicher Informationsverarbeitung kategorisiert. Daraus ergeben sich verschiedene Methoden um Repräsentationen unterschiedlicher Modalitäten miteinander zu kombinieren. Ausgehend von diesen methodischen Grundlagen wird diskutiert, wie verschiedene Forschungsprobleme in der automatischen Sprachverarbeitung von multimodaler Verankerung profitieren können und welche Herausforderungen sich dabei ergeben. Ein besonderer Schwerpunkt wird dabei auf die multimodale konzeptuelle Verankerung von Verben gelegt, da diese eine wichtige kompositorische Funktion erfüllen.• The work by Lisa Beinborn has been carried out during her affiliation with Ubiquitous Knowledge Processing Lab (UKP) and Research Training Group AIPHES, Technische Universität Darmstadt. * The first and the second authors contributed equally to this work. This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/ | Multimodal Grounding for Language Processing |
d51875405 | Distantly supervised open-domain question answering (DS-QA) aims to find answers in collections of unlabeled text. Existing DS-QA models usually retrieve related paragraphs from a large-scale corpus and apply reading comprehension technique to extract answers from the most relevant paragraph. They ignore the rich information contained in other paragraphs. Moreover, distant supervision data inevitably accompanies with the wrong labeling problem, and these noisy data will substantially degrade the performance of DS-QA. To address these issues, we propose a novel DS-QA model which employs a paragraph selector to filter out those noisy paragraphs and a paragraph reader to extract the correct answer from those denoised paragraphs. Experimental results on real-world datasets show that our model can capture useful information from noisy data and achieve significant improvements on DS-QA as compared to all baselines. The source code and data of this paper can be obtained from https: //github.com/thunlp/OpenQA | Denoising Distantly Supervised Open-Domain Question Answering |
d219299945 | We show that it is straightforward to train a state of the art named entity tagger (spaCy) to recognize political actors in Dutch parliamentary proceedings with high accuracy. The tagger was trained on 3.4K manually labeled examples, which were created in a modest 2.5 days work. This resource is made available on github. Besides proper nouns of persons and political parties, the tagger can recognize quite complex definite descriptions referring to cabinet ministers, ministries, and parliamentary committees. We also provide a demo search engine which employs the tagged entities in its SERP and result summaries. | Who Mentions Whom? Recognizing Political Actors in Proceedings |
d218973994 | The Princeton WordNet, while one of the most widely used resources for NLP, has not been updated for a long time, and as such a new project English WordNet has arisen to continue the development of the model under an open-source paradigm. In this paper, we detail the second release of this resource entitled "English WordNet 2020". The work has focused firstly, on the introduction of new synsets and senses and developing guidelines for this and secondly, on the integration of contributions from other projects. We present the changes in this edition, which total over 15,000 changes over the previous release. | English WordNet 2020: Improving and Extending a WordNet for English using an Open-Source Methodology |
d51862727 | Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information. While specialized models have been developed for particular applications, few are widely used in practice, as customization typically requires derivation of a custom inference algorithm. In this paper, we build on recent advances in variational inference methods and propose a general neural framework, based on topic models, to enable flexible incorporation of metadata and allow for rapid exploration of alternative models. Our approach achieves strong performance, with a manageable tradeoff between perplexity, coherence, and sparsity. Finally, we demonstrate the potential of our framework through an exploration of a corpus of articles about US immigration. | Neural Models for Documents with Metadata |
d15128029 | We consider the structural descriptions produced by various grammatical formalisms in ~ of the complexity of the paths and the relationship between paths in the sets of structural descriptions that each system can generate. In considering the relationship between formalisms, we show that it is useful to abstract away from the details of the formalism, and examine the nature of their derivation process as reflected by properties of their deriva:ion trees. We find that several of the formalisms considered can be seen as being closely related since they have derivation tree sets with the same structure as those produced by Context-Free C-ramma~. On the basis of this observation, we describe a class of formalisms which we call Linear Context-Free Rewriting Systems, and show they are recognizable in polynomial time and generate only semilinear languages. | CHARACTERIZING STRUCTURAL DESCRIPTIONS PRODUCED BY VARIOUS. GRAMMATICAL FORMALISMS* |
d14363217 | We present a new approach for documentlevel sentiment inference, where the goal is to predict directed opinions (who feels positively or negatively towards whom) for all entities mentioned in a text. To encourage more complete and consistent predictions, we introduce an ILP that jointly models (1) sentence-and discourse-level sentiment cues, (2) factual evidence about entity factions, and (3) global constraints based on social science theories such as homophily, social balance, and reciprocity. Together, these cues allow for rich inference across groups of entities, including for example that CEOs and the companies they lead are likely to have similar sentiment towards others. We evaluate performance on new, densely labeled data that provides supervision for all pairs, complementing previous work that only labeled pairs mentioned in the same sentence. Experiments demonstrate that the global model outperforms sentence-level baselines, by providing more coherent predictions across sets of related entities. | Document-level Sentiment Inference with Social, Faction, and Discourse Context |
d226222116 | Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings. However, people can grasp new knowledge by learning a few instances. To this end, we take the first step to study the few-shot relational triple extraction, which has not been well understood. Unlike previous single-task few-shot problems, relational triple extraction is more challenging as the entities and relations have implicit correlations. In this paper, We propose a novel multi-prototype embedding network model to jointly extract the composition of relational triples, namely, entity pairs and corresponding relations. To be specific, we design a hybrid prototypical learning mechanism that bridges text and knowledge concerning both entities and relations. Thus, implicit correlations between entities and relations are injected. Additionally, we propose a prototype-aware regularization to learn more representative prototypes. Experimental results demonstrate that the proposed method can improve the performance of the few-shot triple extraction. * Equal contribution and shared co-first authorship. † Corresponding author This work is licensed under a Creative Commons Attribution 4.0 International Licence.Licence details: | Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction |
d259076041 | Utilizing language models (LMs) without internal access is becoming an attractive paradigm in the field of NLP as many cutting-edge LMs are released through APIs and boast a massive scale. The de-facto method in this type of black-box scenario is known as prompting, which has shown progressive performance enhancements in situations where data labels are scarce or unavailable. Despite their efficacy, they still fall short in comparison to fully supervised counterparts and are generally brittle to slight modifications. In this paper, we propose Clustering-Enhanced Linear Discriminative Analysis (CELDA), a novel approach that improves the text classification accuracy with a very weak-supervision signal (i.e., name of the labels). Our framework draws a precise decision boundary without accessing weights or gradients of the LM model or data labels. The core ideas of CELDA are twofold: (1) extracting a refined pseudo-labeled dataset from an unlabeled dataset, and (2) training a lightweight and robust model on the top of LM, which learns an accurate decision boundary from an extracted noisy dataset. Throughout in-depth investigations on various datasets, we demonstrated that CELDA reaches new state-of-theart in weakly-supervised text classification and narrows the gap with a fully-supervised model. Additionally, our proposed methodology can be applied universally to any LM and has the potential to scale to larger models, making it a more viable option for utilizing large LMs. | CELDA: Leveraging Black-box Language Model as Enhanced Classifier without Labels |
d2512012 | We develop parsing oracles for two transition-based dependency parsers, including the arc-standard parser, solving a problem that was left open in (Goldberg and Nivre, 2013). We experimentally show that using these oracles during training yields superior parsing accuracies on many languages. | A Tabular Method for Dynamic Oracles in Transition-Based Parsing |
d16677309 | Understanding contextual information is key to detecting metaphors in discourse. Most current work aims at detecting metaphors given a single sentence, thus focusing mostly on local contextual cues within a short text. In this paper, we present a novel approach that explicitly leverages global context of a discourse to detect metaphors. In addition, we show that syntactic information such as dependency structures can help better describe local contextual information, thus improving detection results when combined. We apply our methods on a newly annotated online discussion forum, and show that our approach outperforms the state-of-the-art baselines in previous literature. | Metaphor Detection in Discourse |
d233210339 | Beam search is the go-to method for decoding auto-regressive machine translation models. While it yields consistent improvements in terms of BLEU, it is only concerned with finding outputs with high model likelihood, and is thus agnostic to whatever end metric or score practitioners care about. Our aim is to establish whether beam search can be replaced by a more powerful metricdriven search technique. To this end, we explore numerous decoding algorithms, including some which rely on a value function parameterised by a neural network, and report results on a variety of metrics. Notably, we introduce a Monte-Carlo Tree Search (MCTS) based method and showcase its competitiveness. We provide a blueprint for how to use MCTS fruitfully in language applications, which opens promising future directions. We find that which algorithm is best heavily depends on the characteristics of the goal metric; we believe that our extensive experiments and analysis will inform further research in this area. | Machine Translation Decoding beyond Beam Search |
d258486847 | Towards computational systems capable of dealing with complex and general linguistic phenomena, it is essential to understand figurative language, which verbal humor is an instance of. This paper reports state-of-the-art results for Humor Recognition in Portuguese, specifically, an F1-score of 99.6% with a BERTbased classifier. However, following the surprising high performance in such a challenging task, we further analyzed what was actually learned by the classifiers. Our main conclusions were that classifiers based on contentfeatures achieve the best performance, but rely mostly on stylistic aspects of the text, not necessarily related to humor, such as punctuation and question words. On the other hand, for humor-related features, we identified some important aspects, such as the presence of named entities, ambiguity and incongruity. | What do Humor Classifiers Learn? An Attempt to Explain Humor Recognition Models |
d258486899 | This paper investigates whether adding data of typologically closer languages improves the performance of transformer-based models for three different downstream tasks, namely Partof-Speech tagging, Named Entity Recognition, and Sentiment Analysis, compared to a monolingual and plain multilingual language model. For the presented pilot study, we performed experiments for the use case of Slovene, a low(er)-resourced language belonging to the Slavic language group. The experiments were carried out in a controlled setting, where a monolingual model for Slovene was compared to combined language models containing Slovene, trained with the same amount of Slovene data. The experimental results show that adding typologically closer languages indeed improves the performance of the Slovene language model, and even succeeds in outperforming the large multilingual XLM-RoBERTa model for NER and PoStagging. We also reveal that, contrary to intuition, distant or unrelated languages also combine admirably with Slovene, often outperforming XLM-R as well. All the bilingual models used in the experiments are publicly available. 1 | Too Many Cooks Spoil the Model: Are Bilingual Models for Slovene Better than a Large Multilingual Model? |
d14108286 | We explore a stacked framework for learning to predict dependency structures for natural language sentences. A typical approach in graph-based dependency parsing has been to assume a factorized model, where local features are used but a global function is optimized(McDonald et al., 2005b). RecentlyNivre and McDonald (2008)used the output of one dependency parser to provide features for another. We show that this is an example of stacked learning, in which a second predictor is trained to improve the performance of the first. Further, we argue that this technique is a novel way of approximating rich non-local features in the second parser, without sacrificing efficient, model-optimal prediction. Experiments on twelve languages show that stacking transition-based and graphbased parsers improves performance over existing state-of-the-art dependency parsers. | Stacking Dependency Parsers |
d14221870 | Many shallow natural language understanding tasks use dependency trees to extract relations between content words. However, strict surface-structure dependency trees tend to follow the linguistic structure of sentences too closely and frequently fail to provide direct relations between content words. To mitigate this problem, the original Stanford Dependencies representation also defines two dependency graph representations which contain additional and augmented relations that explicitly capture otherwise implicit relations between content words. In this paper, we revisit and extend these dependency graph representations in light of the recent Universal Dependencies (UD) initiative and provide a detailed account of an enhanced and an enhanced++ English UD representation. We further present a converter from constituency to basic, i.e., strict surface structure, UD trees, and a converter from basic UD trees to enhanced and enhanced++ English UD graphs. We release both converters as part of Stanford CoreNLP and the Stanford Parser. | Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks |
d14171478 | The string regeneration problem is the problem of generating a fluent sentence from a bag of words. We explore the Ngram language model approach to string regeneration. The approach computes the highest probability permutation of the input bag of words under an N-gram language model. We describe a graph-based approach for finding the optimal permutation. The evaluation of the approach on a number of datasets yielded promising results, which were confirmed by conducting a manual evaluation study. | A Graph-Based Approach to String Regeneration |
d243865271 | Multimodal Transformers achieve superior performance in multimodal learning tasks. However, the quadratic complexity of the selfattention mechanism in Transformers limits their deployment in low-resource devices and makes their inference and training computationally expensive. We propose multimodal Sparse Phased Transformer (SPT) to alleviate the problem of self-attention complexity and memory footprint. SPT uses a sampling function to generate a sparse attention matrix and compress a long sequence to a shorter sequence of hidden states. SPT concurrently captures interactions between the hidden states of different modalities at every layer. To further improve the efficiency of our method, we use Layer-wise parameter sharing and Factorized Co-Attention that share parameters between Cross Attention Blocks, with minimal impact on task performance. We evaluate our model with three sentiment analysis datasets and achieve comparable or superior performance compared with the existing methods, with a 90% reduction in the number of parameters. We conclude that (SPT) along with parameter sharing can capture multimodal interactions with reduced model size and improved sample efficiency. | Multimodal Phased Transformer for Sentiment Analysis |
d243865557 | Natural language generation (NLG) tasks on pro-drop languages are known to suffer from zero pronoun (ZP) problems, and the problems remain challenging due to the scarcity of ZP-annotated NLG corpora. In this case, we propose a highly adaptive two-stage approach to couple context modeling with ZP recovering to mitigate the ZP problem in NLG tasks. Notably, we frame the recovery process in a task-supervised fashion where the ZP representation recovering capability is learned during the NLG task learning process, thus our method does not require NLG corpora annotated with ZPs. For system enhancement, we learn an adversarial bot to adjust our model outputs to alleviate the error propagation caused by mis-recovered ZPs. Experiments on three document-level NLG tasks, i.e., machine translation, question answering, and summarization, show that our approach can improve the performance to a great extent, and the improvement on pronoun translation is very impressive. | Coupling Context Modeling with Zero Pronoun Recovering for Document-Level Natural Language Generation |
d15627896 | Information extraction in specialized texts raises different problems related to the kind of searched information. In this paper, we are interested in relation identification between some concepts in medical reports, task that was evaluated in the i2b2 2010 challenge. As relations are expressed in natural language with a great variety of forms, we proceeded to sentence analysis by extracting features that enable all together to identify a relation and we modeled this task as a multi-class classification based on an SVM, each type of relation representing a class. We will present the selection of the features used by our system and an error analysis. This approach allowed us to obtain an F-measure of 0.70, classifying the system among the best systems. | Multi-Class SVM for Relation Extraction from Clinical Reports |
d15831730 | Verb Phrase Ellipsis (VPE) is an anaphoric construction in which a verb phrase has been elided. It occurs frequently in dialogue and informal conversational settings, but despite its evident impact on event coreference resolution and extraction, there has been relatively little work on computational methods for identifying and resolving VPE. Here, we present a novel approach to detecting and resolving VPE by using supervised discriminative machine learning techniques trained on features extracted from an automatically parsed, publicly available dataset. Our approach yields state-of-the-art results for VPE detection by improving F1 score by over 11%; additionally, we explore an approach to antecedent identification that uses the Margin-Infused-Relaxed-Algorithm, which shows promising results. | Verb Phrase Ellipsis Resolution Using Discriminative and Margin-Infused Algorithms |
d52159416 | Neural conversation models tend to generate safe, generic responses for most inputs. This is due to the limitations of likelihoodbased decoding objectives in generation tasks with diverse outputs, such as conversation.To address this challenge, we propose a simple yet effective approach for incorporating side information in the form of distributional constraints over the generated responses. We propose two constraints that help generate more content rich responses that are based on a model of syntax and topics(Griffiths et al., 2005)and semantic similarity (Arora et al., 2016). We evaluate our approach against a variety of competitive baselines, using both automatic metrics and human judgments, showing that our proposed approach generates responses that are much less generic without sacrificing plausibility. A working demo of our code can be found at https://github.com/abaheti95/ DC-NeuralConversation. | Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints |
d237532398 | The absence of labeled data for training neural models is often addressed by leveraging knowledge about the specific task, resulting in heuristic but noisy labels. The knowledge is captured in labeling functions, which detect certain regularities or patterns in the training samples and annotate corresponding labels for training. This process of weakly supervised training may result in an over-reliance on the signals captured by the labeling functions and hinder models to exploit other signals or to generalize well. We propose KnowMAN, an adversarial scheme that enables to control influence of signals associated with specific labeling functions. KnowMAN forces the network to learn representations that are invariant to those signals and to pick up other signals that are more generally associated with an output label. KnowMAN strongly improves results compared to direct weakly supervised learning with a pre-trained transformer language model and a feature-based baseline. | KnowMAN: Weakly Supervised Multinomial Adversarial Networks |
d249062729 | Current image description generation models do not transfer well to the task of describing human faces. To encourage the development of more human-focused descriptions, we developed a new data set of facial descriptions based on the CelebA image data set. We describe the properties of this data set, and present results from a face description generator trained on it, which explores the feasibility of using transfer learning from VGGFace/ResNet CNNs. Comparisons are drawn through both automated metrics and human evaluation by 76 English-speaking participants. The descriptions generated by the VGGFace-LSTM + Attention model are closest to the ground truth according to human evaluation whilst the ResNet-LSTM + Attention model obtained the highest CIDEr and CIDEr-D results (1.252 and 0.686 respectively). Together, the new data set and these experimental results provide data and baselines for future work in this area. | Face2Text revisited: Improved data set and baseline results |
d260816720 | Interactive question answering systems should allow users to lead a coherent information seeking dialogue. Compared with systems that only locally evaluate a question, interactive systems facilitate the information seeking process and provide a more natural feel. We show that by extending a QA system to handle several types of anaphora and ellipsis, the naturalness of the interaction can be considerably improved. We describe an implementation in our prototype QA system for German and give a walk-through example of the enhanced interaction capabilities. | Towards Natural Interactive Question Answering |
d253157531 | Embedding knowledge graphs (KGs) for multihop logical reasoning is a challenging problem due to massive and complicated structures in many KGs. Recently, many promising works projected entities and queries into a geometric space to efficiently find answers. However, it remains challenging to model the negation and union operator. The negation operator has no strict boundaries, which generates overlapped embeddings and leads to obtaining ambiguous answers. An additional limitation is that the union operator is non-closure, which undermines the model to handle a series of union operators. To address these problems, we propose a novel probabilistic embedding model, namely Gamma Embeddings (GammaE), for encoding entities and queries to answer different types of FOL queries on KGs. We utilize the linear property and strong boundary support of the Gamma distribution to capture more features of entities and queries, which dramatically reduces model uncertainty. Furthermore, Gam-maE implements the Gamma mixture method to design the closed union operator. The performance of GammaE is validated on three large logical query datasets. Experimental results show that GammaE significantly outperforms state-of-the-art models on public benchmarks. | GammaE: Gamma Embeddings for Logical Queries on Knowledge Graphs |
d253098468 | In this paper, we present the results and findings of the Shared Task on Gender Rewriting, which was organized as part of the Seventh Arabic Natural Language Processing Workshop. The task of gender rewriting refers to generating alternatives of a given sentence to match different target user gender contexts (e.g., female speaker with a male listener, a male speaker with a male listener, etc.). This requires changing the grammatical gender (masculine or feminine) of certain words referring to the users. In this task, we focus on Arabic, a gender-marking morphologically rich language. A total of five teams from four countries participated in the shared task. | The Shared Task on Gender Rewriting |
d253116844 | Prior studies of zero-shot stance detection identify the attitude of texts towards unseen topics occurring in the same document corpus. Such task formulation has three limitations: (i) Single domain/dataset. A system is optimized on a particular dataset from a single domain; therefore, the resulting system cannot work well on other datasets; (ii) the model is evaluated on a limited number of unseen topics; (iii) it is assumed that part of the topics has rich annotations, which might be impossible in real-world applications. These drawbacks will lead to an impractical stance detection system that fails to generalize to open domains and open-form topics.This work defines OpenStance: opendomain zero-shot stance detection, aiming to handle stance detection in an open world with neither domain constraints nor topicspecific annotations. The key challenge of OpenStance lies in the open-domain generalization: learning a system with fully unspecific supervision but capable of generalizing to any dataset. To solve OpenStance, we propose to combine indirect supervision, from textual entailment datasets, and weak supervision, from data generated automatically by pretrained Language Models. Our single system, without any topic-specific supervision, outperforms the supervised method on three popular datasets. To our knowledge, this is the first work that studies stance detection under the open-domain zero-shot setting. All data and code are publicly released. 1 | OpenStance: Real-world Zero-shot Stance Detection |
d259309318 | Lexical complexity prediction (LCP) is the task of predicting the complexity of words in a text on a continuous scale. It plays a vital role in simplifying or annotating complex words to assist readers. To study lexical complexity in Japanese, we construct the first Japanese LCP dataset. Our dataset provides separate complexity scores for Chinese/Korean annotators and others to address the readers' L1-specific needs. In the baseline experiment, we demonstrate the effectiveness of a BERT-based system for Japanese LCP. | Japanese Lexical Complexity for Non-Native Readers: A New Dataset |
d182952931 | In this paper, we present a novel approach for incorporating external knowledge in Recurrent Neural Networks (RNNs). We propose the integration of lexicon features into the self-attention mechanism of RNN-based architectures. This form of conditioning on the attention distribution, enforces the contribution of the most salient words for the task at hand. We introduce three methods, namely attentional concatenation, feature-based gating and affine transformation. Experiments on six benchmark datasets show the effectiveness of our methods. Attentional feature-based gating yields consistent performance improvement across tasks. Our approach is implemented as a simple add-on module for RNN-based models with minimal computational overhead and can be adapted to any deep neural architecture. | Attention-based Conditioning Methods for External Knowledge Integration |
d11353722 | This paper proposes an unsupervised word segmentation algorithm that identifies word boundaries in continuous source language text in order to improve the translation quality of statistical machine translation (SMT) approaches.The method can be applied to any language pair where the source language is unsegmented and the target language segmentation is known. First, an iterative bootstrap method is applied to learn multiple segmentation schemes that are consistent with the phrasal segmentations of an SMT system trained on the resegmented bitext. In the second step, multiple segmentation schemes are integrated into a single SMT system by characterizing the source language side and merging identical translation pairs of differently segmented SMT models. Experimental results translating five Asian languages into English revealed that the method of integrating multiple segmentation schemes outperforms SMT models trained on any of the learned word segmentations and performs comparably to available state-ofthe-art monolingually-built segmentation tools. | Integration of Multiple Bilingually-Learned Segmentation Schemes into Statistical Machine Translation |
d252815919 | This paper presents work on novel machine translation (MT) systems between spoken and signed languages, where signed languages are represented in SignWriting, a sign language writing system. Our work 1 seeks to address the lack of out-of-the-box support for signed languages in current MT systems and is based on the SignBank dataset, which contains pairs of spoken language text and SignWriting content. We introduce novel methods to parse, factorize, decode, and evaluate SignWriting, leveraging ideas from neural factored MT. In a bilingual setup-translating from American Sign Language to (American) English-our method achieves over 30 BLEU, while in two multilingual setups-translating in both directions between spoken languages and signed languages-we achieve over 20 BLEU. We find that common MT techniques used to improve spoken language translation similarly affect the performance of sign language translation. These findings validate our use of an intermediate text representation for signed languages to include them in NLP research. | Machine Translation between Spoken Languages and Signed Languages Represented in SignWriting |
d252624591 | In this paper, we present HIBOU, an eBook application initially developed for iOs, displaying adapted texts (i.e. simplified), and proposing text comprehension activities. The application has been used in six elementary schools in France to evaluate and train reading fluency and comprehension skills on beginning readers of French. HIBOU displays two versions of French literary and documentary texts from the ALECTOR corpus, the 'original', and a simplified version. Text simplifications have been manually performed at the lexical, syntactic, and discursive levels. The child can read in autonomy and has access to different games on word identification. HIBOU is at present being developed to be online in a platform that will be available at elementary schools in France. | HIBOU: an eBook to improve Text Comprehension and Reading Fluency for Beginning Readers of French |
d53083786 | Many important entity types in web documents, such as dates, times, email addresses, and course numbers, follow or closely resemble patterns that can be described by Regular Expressions (REs). Due to a vast diversity of web documents and ways in which they are being generated, even seemingly straightforward tasks such as identifying mentions of date in a document become very challenging. It is reasonable to claim that it is impossible to create a RE that is capable of identifying such entities from web documents with perfect precision and recall. Rather than abandoning REs as a go-to approach for entity detection, this paper explores ways to combine the expressive power of REs, ability of deep learning to learn from large data, and human-in-the loop approach into a new integrated framework for entity identification from web data. The framework starts by creating or collecting the existing REs for a particular type of an entity. Those REs are then used over a large document corpus to collect weak labels for the entity mentions and a neural network is trained to predict those RE-generated weak labels. Finally, a human expert is asked to label a small set of documents and the neural network is fine tuned on those documents. The experimental evaluation on several entity identification problems shows that the proposed framework achieves impressive accuracy, while requiring very modest human effort. | Regular Expression Guided Entity Mention Mining from Noisy Web Data |
d1260035 | In addition to information, text contains attitudinal, and more specifically, emotional content. This paper explores the text-based emotion prediction problem empirically, using supervised machine learning with the SNoW learning architecture. The goal is to classify the emotional affinity of sentences in the narrative domain of children's fairy tales, for subsequent usage in appropriate expressive rendering of text-to-speech synthesis. Initial experiments on a preliminary data set of 22 fairy tales show encouraging results over a naïve baseline and BOW approach for classification of emotional versus non-emotional contents, with some dependency on parameter tuning. We also discuss results for a tripartite model which covers emotional valence, as well as feature set alternations. In addition, we present plans for a more cognitively sound sequential model, taking into consideration a larger set of basic emotions. | Emotions from text: machine learning for text-based emotion prediction |
d1366474 | The task of Named Entity Recognition (NER) is to identify in text predefined units of information such as person names, organizations and locations. In this work, we address the problem of NER in Estonian using supervised learning approach. We explore common issues related to building a NER system such as the usage of language-agnostic and languagespecific features, the representation of named entity tags, the required corpus size and the need for linguistic tools. For system training and evaluation purposes, we create a gold standard NER corpus. On this corpus, our CRF-based system achieves an overall F 1 -score of 87%. | Named Entity Recognition in Estonian |
d8589715 | We investigate methods for evaluating agreement among a relatively large group of annotators who have not received extensive training and differ in terms of ability and motivation. We show that it is possible to isolate a reliable subgroup of annotators, so that aspects of the difficulty of the underlying task can be studied. Our task is to annotate the argumentative structure of short texts. | Ranking the annotators: An agreement study on argumentation structure |
d21688828 | We argue that extrapolation to examples outside the training space will often be easier for models that capture global structures, rather than just maximise their local fit to the training data. We show that this is true for two popular models: the Decomposable Attention Model and word2vec. | Extrapolation in NLP |
d248505837 | Training mixed-domain translation models is a complex task that demands tailored architectures and costly data preparation techniques. In this work, we leverage federated learning (FL) in order to tackle the problem. Our investigation demonstrates that with slight modifications in the training process, neural machine translation (NMT) engines can be easily adapted when an FL-based aggregation is applied to fuse different domains. Experimental results also show that engines built via FL are able to perform on par with state-of-the-art baselines that rely on centralized training techniques.We evaluate our hypothesis in the presence of five datasets with different sizes, from different domains, to translate from German into English and discuss how FL and NMT can mutually benefit from each other. In addition to providing benchmarking results on the union of FL and NMT, we also propose a novel technique to dynamically control the communication bandwidth by selecting impactful parameters during FL updates. This is a significant achievement considering the large size of NMT engines that need to be exchanged between FL parties. | Training Mixed-Domain Translation Models via Federated Learning |
d173188642 | Sentiment analysis has a range of corpora available across multiple languages. For emotion analysis, the situation is more limited, which hinders potential research on crosslingual modeling and the development of predictive models for other languages. In this paper, we fill this gap for German by constructing deISEAR, a corpus designed in analogy to the well-established English ISEAR emotion dataset. Motivated by Scherer's appraisal theory, we implement a crowdsourcing experiment which consists of two steps. In step 1, participants create descriptions of emotional events for a given emotion. In step 2, five annotators assess the emotion expressed by the texts. We show that transferring an emotion classification model from the original English ISEAR to the German crowdsourced deISEAR via machine translation does not, on average, cause a performance drop. | Crowdsourcing and Validating Event-focused Emotion Corpora for German and English |
d51912795 | Historical text normalization suffers from small datasets that exhibit high variance, and previous work has shown that multitask learning can be used to leverage data from related problems in order to obtain more robust models. Previous work has been limited to datasets from a specific language and a specific historical period, and it is not clear whether results generalize. It therefore remains an open problem, when historical text normalization benefits from multi-task learning. We explore the benefits of multi-task learning across 10 different datasets, representing different languages and periods. Our main findingcontrary to what has been observed for other NLP tasks-is that multi-task learning mainly works when target task data is very scarce. | Multi-task learning for historical text normalization: Size matters |
d236460111 | Reducing and counter-acting hate speech on Social Media is a significant concern. Most of the proposed automatic methods are conducted exclusively on English and very few consistently labeled, non-English resources have been proposed. Learning to detect hate speech on English and transferring to unseen languages seems an immediate solution. This work is the first to shed light on the limits of this zero-shot, cross-lingual transfer learning framework for hate speech detection. We use benchmark data sets in English, Italian, and Spanish to detect hate speech towards immigrants and women. Investigating post-hoc explanations of the model, we discover that nonhateful, language-specific taboo interjections are misinterpreted as signals of hate speech. Our findings demonstrate that zero-shot, crosslingual models cannot be used as they are, but need to be carefully designed. | Exposing the limits of Zero-shot Cross-lingual Hate Speech Detection |
d252873563 | We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks. We focus on approaches that can be employed in training end-to-end information extraction systems, and find there is no definitive solution without considering task properties, and provide our observations to help with future design choices: 1) a tagging approach often yields higher precision while span enumeration and boundary prediction provide higher recall; 2) span type information can benefit a boundary prediction approach; 3) additional contextualization does not help span finding in most cases. | An Empirical Study on Finding Spans |
d634349 | The paper describes an XML annotation format and tool developed within the MUCHMORE project. The annotation scheme was designed specifically for the purposes of Cross-Lingual Information Retrieval in the medical domain so as to allow both efficient and flexible access to layers of information. We use a parallel English-German corpus of medical abstracts and annotate it with linguistic information (tokenisation, part-of-speech tagging, lemmatisation and decomposition, phrase recognition, grammatical functions) as well as semantic information from various sources. The annotation of medical terms/concepts, semantic types and semantic relations is based on the Unified Medical Language System (UMLS). Additionally, we use EuroWordNet as a general-language resource in annotating word senses and to compare domain-specific and general language use. A major aim of the project is also to complement existing ontological resources by extracting new terms and new semantic relations. We present the annotation scheme, which is conceptually related to stand-off annotation, and describe our tool for automatic semantic annotation. | An Efficient and Flexible Format for Linguistic and Semantic Annotation |
d241583774 | Cross-lingual summarization is a challenging task for which there are no cross-lingual scientific resources currently available. To overcome the lack of a high-quality resource, we present a new dataset for monolingual and cross-lingual summarization considering the English-German pair. We collect high-quality, real-world cross-lingual data from Spektrum der Wissenschaft, which publishes humanwritten German scientific summaries of English science articles on various subjects. The generated Spektrum dataset is small; therefore, we harvest a similar dataset from the Wikipedia Science Portal to complement it. The Wikipedia dataset consists of English and German articles, which can be used for monolingual and cross-lingual summarization. Furthermore, we present a quantitative analysis of the datasets and results of empirical experiments with several existing extractive and abstractive summarization models. The results suggest the viability and usefulness of the proposed dataset for monolingual and crosslingual summarization. | A Novel Wikipedia based Dataset for Monolingual and Cross-Lingual Summarization |
d43895401 | Future improvement of machine translation systems requires reliable automatic evaluation and error classification measures to avoid time and money consuming human classification. In this article, we propose a new method for automatic error classification and systematically compare its results to those obtained by humans. We show that the proposed automatic measures correlate well with human judgments across different error classes as well as across different translation outputs on four out of five commonly used error classes. | From Human to Automatic Error Classification for Machine Translation Output |
d53080764 | Emotions are expressed in nuanced ways, which varies by collective or individual experiences, knowledge, and beliefs. Therefore, to understand emotion, as conveyed through text, a robust mechanism capable of capturing and modeling different linguistic nuances and phenomena is needed. We propose a semisupervised, graph-based algorithm to produce rich structural descriptors which serve as the building blocks for constructing contextualized affect representations from text. The pattern-based representations are further enriched with word embeddings and evaluated through several emotion recognition tasks. Our experimental results demonstrate that the proposed method outperforms state-of-the-art techniques on emotion recognition tasks. | CARER: Contextualized Affect Representations for Emotion Recognition |
d215745521 | In this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition. First, we introduce the task in detail, showing the relationship with coreference recognition and differences from the existing tasks. Second, we propose an end-to-end model for the task, handling mentions as well as coreference relationship jointly. The model exploits the state-of-the-art contextualized BERT representations as the encoder, and is further enhanced with the sememe knowledge from HowNet by graph attention networks. We manually annotate a benchmark dataset for the task and then conduct experiments on it. Results demonstrate that our final model is effective and competitive for the task. Detailed analysis is offered for comprehensively understanding the new task and our proposed model. | End to End Chinese Lexical Fusion Recognition with Sememe Knowledge |
d237532546 | The creation of a quality summarization dataset is an expensive, time-consuming effort, requiring the production and evaluation of summaries by both trained humans and machines. The returns to such an effort would increase significantly if the dataset could be used in additional languages without repeating human annotations. To investigate how much we can trust machine translation of summarization datasets, we translate the English Sum-mEval dataset to seven languages and compare performances across automatic evaluation measures. We explore equivalence testing as the appropriate statistical paradigm for evaluating correlations between human and automated scoring of summaries. We also consider the effect of translation on the relative performance between measures. We find some potential for dataset reuse in languages similar to the source and along particular dimensions of summary quality. Our code and data can be found at https://github.com/ PrimerAI/primer-research/. | Does Summary Evaluation Survive Translation to Other Languages? |
d226965687 | Referring expression generation (REG) algorithms offer computational models of the production of referring expressions. In earlier work, a corpus of referring expressions (REs) in Mandarin was introduced. In the present paper, we annotate this corpus, evaluate classic REG algorithms on it, and compare the results with earlier results on the evaluation of REG for English referring expressions. Next, we offer an in-depth analysis of the corpus, focusing on issues that arise from the grammar of Mandarin. We discuss shortcomings of previous REG evaluations that came to light during our investigation and we highlight some surprising results. Perhaps most strikingly, we found a much higher proportion of under-specified expressions than previous studies had suggested, not just in Mandarin but in English as well. | Lessons from Computational Modelling of Reference Production in Mandarin and English |
d5990443 | Chinese spelling check (CSC) is still an open problem today. To the best of our knowledge, language modeling is widely used in CSC because of its simplicity and fair predictive power, but most systems only use the conventional n-gram models. Our work in this paper continues this general line of research by further exploring different ways to glean extra semantic clues and Web resources to enhance the CSC performance in an unsupervised fashion. Empirical results demonstrate the utility of our CSC system. | A Study of Language Modeling for Chinese Spelling Check |
d252819061 | This paper explores the task of identifying the overall sentiment expressed towards volitional entities (persons and organizations) in a document -what we refer to as Entity-Level Sentiment Analysis (ELSA). While identifying sentiment conveyed towards an entity is well researched for shorter texts like tweets, we find little to no research on this specific task for longer texts with multiple mentions and opinions towards the same entity. This lack of research would be understandable if ELSA can be derived from existing tasks and models. To assess this, we annotate a set of professional reviews for their overall sentiment towards each volitional entity in the text. We sample from data already annotated for documentlevel, sentence-level, and target-level sentiment in a multi-domain review corpus, and our results indicate that there is no single proxy task that provides this overall sentiment we seek for the entities at a satisfactory level of performance. We present a suite of experiments aiming to assess the contribution towards ELSA provided by document-, sentence-, and targetlevel sentiment analysis, and provide a discussion of their shortcomings. We show that sentiment in our dataset is expressed not only with an entity mention as target, but also towards targets with a sentiment-relevant relation to a volitional entity. In our data, these relations extend beyond anaphoric coreference resolution, and our findings call for further research of the topic. Finally, we also present a survey of previous relevant work. | Entity-Level Sentiment Analysis (ELSA): An exploratory task survey |
d6733826 | Many evaluation issues for grammatical error detection have previously been overlooked, making it hard to draw meaningful comparisons between different approaches, even when they are evaluated on the same corpus. To begin with, the three-way contingency between a writer's sentence, the annotator's correction, and the system's output makes evaluation more complex than in some other NLP tasks, which we address by presenting an intuitive evaluation scheme. Of particular importance to error detection is the skew of the data -the low frequency of errors as compared to non-errors -which distorts some traditional measures of performance and limits their usefulness, leading us to recommend the reporting of raw measurements (true positives, false negatives, false positives, true negatives). Other issues that are particularly vexing for error detection focus on defining these raw measurements: specifying the size or scope of an error, properly treating errors as graded rather than discrete phenomena, and counting non-errors. We discuss recommendations for best practices with regard to reporting the results of system evaluation for these cases, recommendations which depend upon making clear one's assumptions and applications for error detection. By highlighting the problems with current error detection evaluation, the field will be better able to move forward. | Problems in Evaluating Grammatical Error Detection Systems |
d216056453 | We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework. ESPnet-ST is a new project inside end-toend speech processing toolkit, ESPnet, which integrates or newly implements automatic speech recognition, machine translation, and text-to-speech functions for speech translation. We provide all-in-one recipes including data pre-processing, feature extraction, training, and decoding pipelines for a wide range of benchmark datasets. Our reproducible results can match or even outperform the current state-of-the-art performances; these pretrained models are downloadable. The toolkit is publicly available at https://github. com/espnet/espnet. | ESPnet-ST: All-in-One Speech Translation Toolkit |
d259263977 | In this paper we address the scarcity of annotated data for NArabizi, a Romanized form of North African Arabic used mostly on social media, which poses challenges for Natural Language Processing (NLP). We introduce an enriched version of NArabizi Treebank (Seddah et al., 2020) with three main contributions: the addition of two novel annotation layers (named entity recognition and offensive language detection) and a re-annotation of the tokenization, morpho-syntactic and syntactic layers that ensure annotation consistency. Our experimental results, using different tokenization schemes, showcase the value of our contributions and highlight the impact of working with non-gold tokenization for NER and dependency parsing. To facilitate future research, we make these annotations publicly available. Our enhanced NArabizi Treebank paves the way for creating sophisticated language models and NLP tools for this under-represented language. | Enriching the NArabizi Treebank: A Multifaceted Approach to Supporting an Under-Resourced Language |
d232290704 | Multiple studies have demonstrated that behavior on internet-based social media platforms can be indicative of an individual's mental health status. The widespread availability of such data has spurred interest in mental health research from a computational lens. While previous research has raised concerns about possible biases in models produced from this data, no study has quantified how these biases actually manifest themselves with respect to different demographic groups, such as gender and racial/ethnic groups. Here, we analyze the fairness of depression classifiers trained on Twitter data with respect to gender and racial demographic groups. We find that model performance systematically differs for underrepresented groups and that these discrepancies cannot be fully explained by trivial data representation issues. Our study concludes with recommendations on how to avoid these biases in future research. | Gender and Racial Fairness in Depression Research using Social Media |
d3541996 | We explore two solutions to the problem of mistranslating rare words in neural machine translation. First, we argue that the standard output layer, which computes the inner product of a vector representing the context with all possible output word embeddings, rewards frequent words disproportionately, and we propose to fix the norms of both vectors to a constant value. Second, we integrate a simple lexical module which is jointly trained with the rest of the model. We evaluate our approaches on eight language pairs with data sizes ranging from 100k to 8M words, and achieve improvements of up to +4.3 BLEU, surpassing phrasebased translation in nearly all settings. 1 | Improving Lexical Choice in Neural Machine Translation |
d258564290 | Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task. Given that events are complicated, an event may have different causes under various contexts. Thus, exploiting context plays an essential role in detecting causal relations. Meanwhile, previous works about commonsense causation only consider two events and ignore their context, simplifying the task formulation. This paper proposes a new task to detect commonsense causation between two events in an event sequence (i.e., context), called contextualized commonsense causal reasoning. We also design a zero-shot framework: COLA (Contextualized Commonsense Causality Reasoner) to solve the task from the causal inference perspective. This framework obtains rich incidental supervision from temporality and balances covariates from multiple timestamps to remove confounding effects. Our extensive experiments show that COLA 1 can detect commonsense causality more accurately than baselines. | COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective |
d174799744 | It is well-known that distributional semantic approaches have difficulty in distinguishing between synonyms and antonyms(Grefenstette, 1992;Padó and Lapata, 2003). Recent work has shown that supervision available in English for this task (e.g., lexical resources) can be transferred to other languages via crosslingual word embeddings. However, this kind of transfer misses monolingual distributional information available in a target language, such as contrast relations that are indicative of antonymy (e.g., hot…while…cold). In this work, we improve the transfer by exploiting monolingual information, expressed in the form of co-occurrences with discourse markers that convey contrast. Our approach makes use of less than a dozen markers, which can easily be obtained for many languages. Compared to a baseline using only cross-lingual embeddings, we show absolute improvements of 4-10% F 1 -score in Vietnamese and Hindi. | Combining Discourse Markers and Cross-lingual Embeddings for Synonym-Antonym Classification |
d174799852 | The idea of subword-based word embeddings has been proposed in the literature, mainly for solving the out-of-vocabulary (OOV) word problem observed in standard word-based word embeddings. In this paper, we propose a method of reconstructing pre-trained word embeddings using subword information that can effectively represent a large number of subword embeddings in a considerably small fixed space. The key techniques of our method are twofold: memory-shared embeddings and a variant of the key-value-query self-attention mechanism. Our experiments show that our reconstructed subword-based embeddings can successfully imitate well-trained word embeddings in a small fixed space while preventing quality degradation across several linguistic benchmark datasets, and can simultaneously predict effective embeddings of OOV words. We also demonstrate the effectiveness of our reconstruction method when we apply them to downstream tasks 1 . | Subword-based Compact Reconstruction of Word Embeddings |
d6801402 | In this paper, we propose a new task for assessing the quality of natural language arguments. The premises of a well-reasoned argument should provide enough evidence for accepting or rejecting its claim. Although this criterion, known as sufficiency, is widely adopted in argumentation theory, there are no empirical studies on its applicability to real arguments. In this work, we show that human annotators substantially agree on the sufficiency criterion and introduce a novel annotated corpus. Furthermore, we experiment with feature-rich SVMs and convolutional neural networks and achieve 84% accuracy for automatically identifying insufficiently supported arguments. The final corpus as well as the annotation guideline are freely available for encouraging future research on argument quality. 1 | Recognizing Insufficiently Supported Arguments in Argumentative Essays |
d6832980 | We propose a novel reordering model for phrase-based statistical machine translation (SMT) that uses a maximum entropy (MaxEnt) model to predicate reorderings of neighbor blocks (phrase pairs). The model provides content-dependent, hierarchical phrasal reordering with generalization based on features automatically learned from a real-world bitext. We present an algorithm to extract all reordering events of neighbor blocks from bilingual data. In our experiments on Chineseto-English translation, this MaxEnt-based reordering model obtains significant improvements in BLEU score on the NIST MT-05 and IWSLT-04 tasks. | Maximum Entropy Based Phrase Reordering Model for Statistical Machine Translation |
d6860396 | We report results on predicting the sense of implicit discourse relations between adjacent sentences in text. Our investigation concentrates on the association between discourse relations and properties of the referring expressions that appear in the related sentences. The properties of interest include coreference information, grammatical role, information status and syntactic form of referring expressions. Predicting the sense of implicit discourse relations based on these features is considerably better than a random baseline and several of the most discriminative features conform with linguistic intuitions. However, these features do not perform as well as lexical features traditionally used for sense prediction. | Using entity features to classify implicit discourse relations |
d174798269 | The article describes our submission to SemEval 2019 Task 8 on Fact-Checking in Community Forums. The systems under discussion participated in Subtask A: decide whether a question asks for factual information, opinion/advice or is just socializing. Our primary submission was ranked as the second one among all participants in the official evaluation phase. The article presents our primary solution: Deeply Regularized Residual Neural Network (DRR NN) with Universal Sentence Encoder embeddings. This is followed by a description of two contrastive solutions based on ensemble methods. | TMLab SRPOL at SemEval-2019 Task 8: Fact Checking in Community Question Answering Forums |
d253510792 | Transformer-based pre-trained language models have demonstrated superior performance on various natural language processing tasks. However, it remains unclear how the skills required to handle these tasks distribute among model parameters. In this paper, we find that after prompt tuning for specific tasks, the activations of some neurons within pre-trained Transformers 1 are highly predictive of the task labels. We dub these neurons skill neurons and confirm they encode task-specific skills by finding that:(1) Skill neurons are crucial for handling tasks. Performances of pre-trained Transformers on a task significantly drop when corresponding skill neurons are perturbed. (2) Skill neurons are task-specific. Similar tasks tend to have similar distributions of skill neurons. Furthermore, we demonstrate the skill neurons are most likely generated in pre-training rather than finetuning by showing that the skill neurons found with prompt tuning are also crucial for other fine-tuning methods freezing neuron weights, such as the adapter-based tuning and BitFit. We also explore the applications of skill neurons, including accelerating Transformers with network pruning and building better transferability indicators. These findings may promote further research on understanding Transformers. The source code can be obtained from https: //github.com/THU-KEG/Skill-Neuron. Levy. 2021. Transformer feed-forward layers are keyvalue memories. In Proceedings of EMNLP, pages 5484-5495. Mitchell A Gordon, Kevin Duh, and Nicholas Andrews. 2020. Compressing bert: Studying the effects of weight pruning on transfer learning. arXiv preprint arXiv:2002.08307. , et al. 2021. Pre-trained models: Past, present and future. AI Open, pages 225-250. | Finding Skill Neurons in Pre-trained Transformer-based Language Models |
d235303672 | Unsupervised cross-lingual word embedding (CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingual corpora. This method relies on the assumption that the two embedding spaces are structurally similar, which does not necessarily hold true in general. In this paper, we argue that using a pseudo-parallel corpus generated by an unsupervised machine translation model facilitates the structural similarity of the two embedding spaces and improves the quality of CLWEs in the unsupervised mapping method. We show that our approach outperforms other alternative approaches given the same amount of data, and, through detailed analysis, we show that data augmentation with the pseudo data from unsupervised machine translation is especially effective for mappingbased CLWEs because (1) the pseudo data makes the source and target corpora (partially) parallel; (2) the pseudo data contains information on the original language that helps to learn similar embedding spaces between the source and target languages. | Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings |
d238857301 | Recent parameter-efficient language model tuning (PELT) methods manage to match the performance of fine-tuning with much fewer trainable parameters and perform especially well when training data is limited. However, different PELT methods may perform rather differently on the same task, making it nontrivial to select the most appropriate method for a specific task, especially considering the fast-growing number of new PELT methods and tasks. In light of model diversity and the difficulty of model selection, we propose a unified framework, UNIPELT, which incorporates different PELT methods as submodules and learns to activate the ones that best suit the current data or task setup via gating mechanism. On the GLUE benchmark, UNIPELT consistently achieves 1~4% gains compared to the best individual PELT method that it incorporates and outperforms fine-tuning under different setups. Moreover, UNIPELT generally surpasses the upper bound that takes the best performance of all its submodules used individually on each task, indicating that a mixture of multiple PELT methods may be inherently more effective than single methods. | UNIPELT: A Unified Framework for Parameter-Efficient Language Model Tuning |
d17367789 | Web debates play an important role in enabling broad participation of constituencies in social, political and economic decision-taking. However, it is challenging to organize, structure, and navigate a vast number of diverse argumentations and comments collected from many participants over a long time period. In this paper we demonstrate Common Round, a next generation platform for large-scale web debates, which provides functions for eliciting the semantic content and structures from the contributions of participants. In particular, Common Round applies language technologies for the extraction of semantic essence from textual input, aggregation of the formulated opinions and arguments. The platform also provides a cross-lingual access to debates using machine translation. | Common Round: Application of Language Technologies to Large-Scale Web Debates |
d256105391 | Domain-adaptive pre-training (or DA-training for short), also known as post-training, aims to train a pre-trained general-purpose language model (LM) using an unlabeled corpus of a particular domain to adapt the LM so that endtasks in the domain can give improved performances. However, existing DA-training methods are in some sense blind as they do not explicitly identify what knowledge in the LM should be preserved and what should be changed by the domain corpus. This paper shows that the existing methods are suboptimal and proposes a novel method to perform a more informed adaptation of the knowledge in the LM by (1) soft-masking the attention heads based on their importance to best preserve the general knowledge in the LM and (2) contrasting the representations of the general and the full (both general and domain knowledge) to learn an integrated representation with both general and domain-specific knowledge. Experimental results will demonstrate the effectiveness of the proposed approach. | Adapting a Language Model While Preserving its General Knowledge |
d256461428 | Recent advances in neural machine translation depend on massive parallel corpora, which are collected from any open source without much guarantee of quality. It stresses the need for noisy corpora filtering, but existing methods are insufficient to solve this issue. They spend much time ensembling multiple scorers trained on clean bitexts, unavailable for low-resource languages in practice. In this paper, we propose a norm-based noisy corpora filtering and refurbishing method with no external data and costly scorers. The noisy and clean samples are separated based on how much information from the source and target sides the model requires to fit the given translation. For the unparallel sentence, the target-side history translation is much more important than the source context, contrary to the parallel ones. The amount of these two information flows can be measured by norms of source-/target-side context vectors. Moreover, we propose to reuse the discovered noisy data by generating pseudo labels via online knowledge distillation. Extensive experiments show that our proposed filtering method performs comparably with state-ofthe-art noisy corpora filtering techniques but is more efficient and easier to operate. Noisy sample refurbishing further enhances the performance by making the most of the given data 1 . * Corresponding author. 1 https://github.com/yulu-dada/Norm_NoisyFiltering wang yong@@ zhi [ 37@@ 69 30@@ 57 18@@ 07 ] is the chief designer in china 's manned spaceflight project . Src: Tgt: Source-side Our metric Target-side [ 37@@ 69 30@@ 57 18@@ 07 ] in China | Norm-based Noisy Corpora Filtering and Refurbishing in Neural Machine Translation |
d260063168 | Some applications of artificial intelligence make it desirable that logical formulae be converted computationally to comprehensible natural language sentences. As there are many logical equivalents to a given formula, finding the most suitable equivalent to be used as input for such a "logic-to-text" generation system is a difficult challenge. In this paper, we focus on the role of brevity: Are the shortest formulae the most suitable? We focus on propositional logic (PL), framing formula minimization (i.e., the problem of finding the shortest equivalent of a given formula) as a Quantified Boolean Formulae (QBFs) satisfiability problem. We experiment with several generators and selection strategies to prune the resulting candidates. We conduct exhaustive automatic and human evaluations of the comprehensibility and fluency of the generated texts. The results suggest that while, in many cases, minimization has a positive impact on the quality of the sentences generated, formula minimization may ultimately not be the best strategy. https://gitlab.nl4xai.eu/eduardo. calo/brevity-PL | Is Shortest Always Best? The Role of Brevity in Logic-to-Text Generation |
d260063191 | This work analyzes backdoor watermarks in an autoregressive transformer fine-tuned to perform a generative sequence-to-sequence task, specifically summarization. We propose and demonstrate an attack to identify trigger words or phrases by analyzing open ended generations from autoregressive models that have backdoor watermarks inserted. It is shown in our work that triggers based on random common words are easier to identify than those based on single, rare tokens. The attack proposed is easy to implement and only requires access to the model weights. Code used to create the backdoor watermarked models and analyze their outputs is shared at https: //github.com/evan-person/ findingBackdoorWatermarks. | GPTs Don't Keep Secrets: Searching for Backdoor Watermark Triggers in Autoregressive Language Models |
d238856913 | Neural rationale models are popular for interpretable predictions of NLP tasks. In these, a selector extracts segments of the input text, called rationales, and passes these segments to a classifier for prediction. Since the rationale is the only information accessible to the classifier, it is plausibly defined as the explanation. Is such a characterization unconditionally correct? In this paper, we argue to the contrary, with both philosophical perspectives and empirical evidence suggesting that rationale models are, perhaps, less rational and interpretable than expected. We call for more rigorous evaluations of these models to ensure desired properties of interpretability are indeed achieved. The code for our experiments is at https://github.com/yimingz89/N eural-Rationale-Analysis. | The Irrationality of Neural Rationale Models |
d259095669 | Amid ongoing health crisis, there is a growing necessity to discern possible signs of Wellness Dimensions (WD) 1 manifested in self-narrated text. As the distribution of WD on social media data is intrinsically imbalanced, we experiment the generative NLP models for data augmentation to enable further improvement in the prescreening task of classifying WD. To this end, we propose a simple yet effective data augmentation approach through promptbased Generative NLP models, and evaluate the ROUGE scores and syntactic/semantic similarity among existing interpretations and augmented data. Our approach with ChatGPT model surpasses all the other methods and achieves improvement over baselines such as Easy-Data Augmentation and Backtranslation. Introducing data augmentation to generate more training samples and balanced dataset, results in the improved F-score and the Matthew's Correlation Coefficient for upto 13.11% and 15.95%, respectively.Wellness Dimensions | Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health |
d256739251 | Pre-trained models based on the Transformer architecture have achieved notable performances in various language processing tasks. This article presents a comparison of two pretrained versions for French in a three-class classification task. The datasets used are of two types: a set of annotated verbatim transcripts from face-to-face interviews conducted during a market study and a set of online posts extracted from a community platform. Little work has been done in these two areas with transcribed oral corpora and online posts in French. | Performance of two French BERT models for French language on verbatim transcripts and online posts |
d233025289 | We propose a new approach to generate multiple variants of the target summary with diverse content and varying lengths, then score and select admissible ones according to users' needs. Abstractive summarizers trained on single reference summaries may struggle to produce outputs that achieve multiple desirable properties, i.e., capturing the most important information, being faithful to the original, grammatical and fluent. In this paper, we propose a two-staged strategy to generate a diverse set of candidate summaries from the source text in stage one, then score and select admissible ones in stage two. Importantly, our generator gives a precise control over the length of the summary, which is especially well-suited when space is limited. Our selectors are designed to predict the optimal summary length and put special emphasis on faithfulness to the original text. Both stages can be effectively trained, optimized and evaluated. Our experiments on benchmark summarization datasets suggest that this paradigm can achieve state-of-the-art performance. | A New Approach to Overgenerating and Scoring Abstractive Summaries |
d244117167 | Warning: this paper discusses and contains content that is offensive or upsetting.The perceived toxicity of language can vary based on someone's identity and beliefs, but this variation is often ignored when collecting toxic language datasets, resulting in dataset and model biases. We seek to understand the who, why, and what behind biases in toxicity annotations. In two online studies with demographically and politically diverse participants, we investigate the effect of annotator identities (who) and beliefs (why), drawing from social psychology research about hate speech, free speech, racist beliefs, political leaning, and more. We disentangle what is annotated as toxic by considering posts with three characteristics: anti-Black language, African American English (AAE) dialect, and vulgarity. Our results show strong associations between annotator identity and beliefs and their ratings of toxicity. Notably, more conservative annotators and those who scored highly on our scale for racist beliefs were less likely to rate anti-Black language as toxic, but more likely to rate AAE as toxic. We additionally present a case study illustrating how a popular toxicity detection system's ratings inherently reflect only specific beliefs and perspectives. Our findings call for contextualizing toxicity labels in social variables, which raises immense implications for toxic language annotation and detection.Text Categories | Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection |
d254877034 | In recent years, there has been a surge of interest in research on automatic mental health detection (MHD) from social media data leveraging advances in natural language processing and machine learning techniques. While significant progress has been achieved in this interdisciplinary research area, the vast majority of work has treated MHD as a binary classification task. The multiclass classification setup is, however, essential if we are to uncover the subtle differences among the statistical patterns of language use associated with particular mental health conditions. Here, we report on experiments aimed at predicting six conditions (anxiety, attention deficit hyperactivity disorder, bipolar disorder, post-traumatic stress disorder, depression, and psychological stress) from Reddit social media posts. We explore and compare the performance of hybrid and ensemble models leveraging transformer-based architectures (BERT and RoBERTa) and BiLSTM neural networks trained on within-text distributions of a diverse set of linguistic features. This set encompasses measures of syntactic complexity, lexical sophistication and diversity, readability, and register-specific ngram frequencies, as well as sentiment and emotion lexicons. In addition, we conduct feature ablation experiments to investigate which types of features are most indicative of particular mental health conditions. | Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media |
d11012792 | This paper presents a method for greatly reducing parse times in LFG by integrating a Constraint Grammar (CG) parser into a probabilistic context-free grammar. The CG parser is used in the pre-processing phase to reduce morphological and lexical ambiguity. Similarly, the c-structure pruning mechanism of XLE is used in the parsing phase to discard low-probability c-structures, before f-annotations are solved. The experiment results show a considerable increase in parsing efficiency and robustness in the annotation of Wolof running text. The Wolof CG parser indicated an f-score of 90% for morphological disambiguation and a speedup of ca. 40%, while the c-structure pruning method increased the speed of the Wolof grammar by over 36%. On a small amount of data, CG disambiguation and c-structure pruning allowed for a speedup of 58%, however with a substantial drop in parse accuracy of 3.62. | Pruning the Search Space of the Wolof LFG Grammar Using a Probabilistic and a Constraint Grammar Parser |
d215744944 | Most existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. A crucial piece of the puzzle that is still missing is to understand how to automate the most elaborate part of the process -generating justifications for verdicts on claims. This paper provides the first study of how these explanations can be generated automatically based on available claim context, and how this task can be modelled jointly with veracity prediction. Our results indicate that optimising both objectives at the same time, rather than training them separately, improves the performance of a fact checking system. The results of a manual evaluation further suggest that the informativeness, coverage and overall quality of the generated explanations are also improved in the multi-task model. | Generating Fact Checking Explanations |
d51889492 | This paper presents our system for "TRAC 2018 Shared Task on Aggression Identification". Our best systems for the English dataset use a combination of lexical and semantic features. However, for Hindi data using only lexical features gave us the best results. We obtained weighted F1measures of 0.5921 for the English Facebook task (ranked 12th), 0.5663 for the English Social Media task (ranked 6th), 0.6292 for the Hindi Facebook task (ranked 1st), and 0.4853 for the Hindi Social Media task (ranked 2nd).This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/. | RiTUAL-UH at TRAC 2018 Shared Task: Aggression Identification |
d9751042 | Recent developments in Natural Language Processing (NLP) are heading towards knowledge rich resources and technology. Integration of linguistically sound grammars, sophisticated machine learning settings and world knowledge background is possible given the availability of the appropriate resources: deep multilingual treebanks, representing detailed syntactic and semantic information; and vast quantities of world knowledge information encoded within ontologies and Linked Open Data datasets (LOD). Thus, the addition of world knowledge facts provides a substantial extension of the traditional semantic resources like WordNet, FrameNet and others. This extension comprises numerous types of Named Entities (Persons, Locations, Events, etc.), their properties (Person has a birthDate; birthPlace, etc.), relations between them (Person works for an Organization), events in which they participated (Person participated in war, etc.), and many other facts. This huge amount of structured knowledge can be considered the missing ingredient of the knowledgebased NLP of 80's and the beginning of 90's.The integration of world knowledge within language technology is defined as an ontology-to-text relation comprising different language and world knowledge in a common model. We assume that the lexicon is based on the ontology, i.e. the word senses are represented by concepts, relations or instances. The problem of lexical gaps is solved by allowing the storage of not only lexica, but also free phrases. The gaps in the ontology (a missing concept for a word sense) are solved by appropriate extensions of the ontology. The mapping is partial in the sense that both elements (the lexicon and the ontology) are artefacts and thusthey are never complete. The integration of the in-terlinked ontology and lexicon with the grammar theory, on the other hand, requires some additional and non-trivial reasoning over the world knowledge. We will discuss phenomena like selectional constraints, metonymy, regular polysemy, bridging relations, which live in the intersective areas between world facts and their language reflection. Thus, the actual text annotation on the basis of ontology-to-text relation requires the explication of additional knowledge like co-occurrence of conceptual information, discourse structure, etc.Such knowledge is mainly present in deeply processed language resources like HPSG-based (LFG-based) treebanks (RedWoods treebank, DeepBank, and others). The inherent characteristics of these language resources is their dynamic nature. They are constructed simultaneously with the development of a deep grammar in the corresponding linguistic formalism. The grammar is used to produce all potential analyses of the sentences within the treebank. The correct analyses are selected manually on the base of linguistic discriminators which would determine the correct linguistic production. The annotation process of the sentences provides feedback for the grammar writer to update the grammar. The life cycle of a dynamic language resource can be naturally supported by the semantic technology behind the ontology and LOD -modeling the grammatical knowledge as well as the annotation knowledge; supporting the annotation process; reclassification after changes within the grammar; querying the available resources; exploitation in real applications. The addition of a LOD component to the system would facilitate the exchange of language resources created in this way and would support the access to the existing resources on the web. | Invited Talk: Ontologies and Linked Open Data for Acquisition and Exploitation of Language Resources |
d237491813 | Most of the recent works on probing representations have focused on BERT, with the presumption that the findings might be similar to the other models. In this work, we extend the probing studies to two other models in the family, namely ELECTRA and XLNet, showing that variations in the pre-training objectives or architectural choices can result in different behaviors in encoding linguistic information in the representations. Most notably, we observe that ELECTRA tends to encode linguistic knowledge in the deeper layers, whereas XLNet instead concentrates that in the earlier layers. Also, the former model undergoes a slight change during fine-tuning, whereas the latter experiences significant adjustments. Moreover, we show that drawing conclusions based on the weight mixing evaluation strategy-which is widely used in the context of layer-wise probing-can be misleading given the norm disparity of the representations across different layers. Instead, we adopt an alternative information-theoretic probing with minimum description length, which has recently been proven to provide more reliable and informative results. | Not All Models Localize Linguistic Knowledge in the Same Place: A Layer-wise Probing on BERToids' Representations |
d258947587 | Paraphrase generation is a long-standing task in natural language processing (NLP). Supervised paraphrase generation models, which rely on human-annotated paraphrase pairs, are costinefficient and hard to scale up. On the other hand, automatically annotated paraphrase pairs (e.g., by machine back-translation), usually suffer from the lack of syntactic diversity -the generated paraphrase sentences are very similar to the source sentences in terms of syntax. In this work, we present PARAAMR, a large-scale syntactically diverse paraphrase dataset created by abstract meaning representation backtranslation. Our quantitative analysis, qualitative examples, and human evaluation demonstrate that the paraphrases of PARAAMR are syntactically more diverse compared to existing large-scale paraphrase datasets while preserving good semantic similarity. In addition, we show that PARAAMR can be used to improve on three NLP tasks: learning sentence embeddings, syntactically controlled paraphrase generation, and data augmentation for few-shot learning. Our results thus showcase the potential of PARAAMR for improving various NLP applications. | ParaAMR: A Large-Scale Syntactically Diverse Paraphrase Dataset by AMR Back-Translation |
d202540557 | Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly because the polarity of an event is not necessarily predictable from its constituent words. In this paper, we propose to propagate affective polarity using discourse relations. Our method is simple and only requires a very small seed lexicon and a large raw corpus. Our experiments using Japanese data show that our method learns affective events effectively without manually labeled data. It also improves supervised learning results when labeled data are small. | Minimally Supervised Learning of Affective Events Using Discourse Relations |
d235368306 | Answering a programming question using only its title is difficult as salient contextual information is omitted. Based on this observation, we present a corpus of over 40,000 StackOverflow question texts to be used in conjunction with their corresponding intents from the CoNaLa dataset . Using both the intent and question body, we use BART to establish a baseline BLEU score of 34.35 for this new task. We find further improvements of 2.8% by combining the mined CoNaLa data with the labeled data to achieve a 35.32 BLEU score. We evaluate prior stateof-the-art CoNaLa models with this additional data and find that our proposed method of using the body and mined data beats the BLEU score of the prior state-of-the-art by 71.96%. Finally, we perform ablations to demonstrate that BART is an unsupervised multimodal learner and examine its extractive behavior. 1 | Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generation |
d44099358 | High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topicrelevant content than a popular sequence-tosequence generation model according to both automatic evaluation and human assessments. | Neural Argument Generation Augmented with Externally Retrieved Evidence |
d235417352 | Semantic parsing is challenging due to the structure gap and the semantic gap between utterances and logical forms. In this paper, we propose an unsupervised semantic parsing method -Synchronous Semantic Decoding (SSD), which can simultaneously resolve the semantic gap and the structure gap by jointly leveraging paraphrasing and grammarconstrained decoding. Specifically, we reformulate semantic parsing as a constrained paraphrasing problem: given an utterance, our model synchronously generates its canonical utterance 1 and meaning representation. During synchronous decoding: the utterance paraphrasing is constrained by the structure of the logical form, therefore the canonical utterance can be paraphrased controlledly; the semantic decoding is guided by the semantics of the canonical utterance, therefore its logical form can be generated unsupervisedly. Experimental results show that SSD is a promising approach and can achieve competitive unsupervised semantic parsing performance on multiple datasets. | From Paraphrasing to Semantic Parsing: Unsupervised Semantic Parsing via Synchronous Semantic Decoding |
d196183669 | Efficiently building an adversarial attacker for natural language processing (NLP) tasks is a real challenge. Firstly, as the sentence space is discrete, it is difficult to make small perturbations along the direction of gradients. Secondly, the fluency of the generated examples cannot be guaranteed. In this paper, we propose MHA, which addresses both problems by performing Metropolis-Hastings sampling, whose proposal is designed with the guidance of gradients. Experiments on IMDB and SNLI show that our proposed MHA outperforms the baseline model on attacking capability. Adversarial training with MHA also leads to better robustness and performance. | Generating Fluent Adversarial Examples for Natural Languages |
d9627392 | This paper describes the method and results of our approach, submitted as team 'NLPCruise' at ALTA shared task 2016. The goal of the shared task is to predict whether two given web Uniform Resource Identifiers (URIs) correspond to the same entity or not. Retrieving the URI content in addition to the dataset provided, we built a two stage filter and match technique that utilises search engine scores, semantic similarity and machine translation features. Our model achieved an F 1 score of 0.85714 on the public test-set and ranked second finally on the private leaderboard. | ALTA Shared-task 2016: Filter and Match Approach to Pair-wise Web URI Linking |
d21721381 | We present a corpus which converts the sense labels of existing Propbank resources to a new unified format which is more compatible with AMR and more robust to sparsity. This adopts an innovation of the Abstract Meaning Representation project(Banarescu et al., 2013)in which one abstracts away from different, related parts of speech, so that related forms such as "insert" and "insertion" could be represented by the same roleset and use the same semantic roles. We note that this conversion also serves to make the different English Propbank corpora released over the years consistent with each other, so that one might train and evaluate systems upon that larger combined data. We present analysis of some appealing characteristics of this final dataset, and present preliminary results of training and evaluating SRL systems on this combined set, to spur usage of this challenging new dataset. | The New Propbank: Aligning Propbank with AMR through POS Unification |
d53060250 | People often create obfuscated language for online communication to avoid Internet censorship, share sensitive information, express strong sentiment or emotion, plan for secret actions, trade illegal products, or simply hold interesting conversations. In this position paper we systematically categorize human-created obfuscated language on various levels, investigate their basic mechanisms, give an overview on automated techniques needed to simulate human encoding. These encoders have potential to frustrate and evade, co-evolve with dynamic human or automated decoders, and produce interesting and adoptable code words. We also summarize remaining challenges for future research on the interaction between Natural Language Processing (NLP) and encryption, and leveraging NLP techniques for encoding and decoding. | Creative Language Encoding under Censorship |
d252460317 | In this paper, we present the methodology of corpus design that will be used to study the comparison of influence between linguistic nudges with positive or negative influences and three conversational agents: robot, smart speaker, and human. We recruited forty-nine participants to form six groups. The conversational agents first asked the participants about their willingness to adopt five ecological habits and invest time and money in ecological problems. The participants were then asked the same questions but preceded by one linguistic nudge, with positive or negative influence. The comparison of standard deviation and mean metrics of differences between these two notes (before the nudge and after) showed that participants were mainly affected by nudges with positive influence, even though several nudges with negative influence decreased the average note. In addition, participants from all groups were willing to spend more money than time on ecological problems. In general, our experiment's early results suggest that a machine agent can influence participants to the same degree as a human agent. A better understanding of the power of influence of different conversational machines and the potential of influence of nudges of different polarities will lead to the development of ethical norms of human-computer interactions. | Corpus Design for Studying Linguistic Nudges in Human-Computer Spoken Interactions |
d7750511 | In this paper we consider the problem of labeling the languages of words in mixed-language documents. This problem is approached in a weakly supervised fashion, as a sequence labeling problem with monolingual text samples for training data. Among the approaches evaluated, a conditional random field model trained with generalized expectation criteria was the most accurate and performed consistently as the amount of training data was varied. | Labeling the Languages of Words in Mixed-Language Documents using Weakly Supervised Methods |
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