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d14255190 | We present a novel approach to automatic metaphor identification, that discovers both metaphorical associations and metaphorical expressions in unrestricted text. Our system first performs hierarchical graph factorization clustering (HGFC) of nouns and then searches the resulting graph for metaphorical connections between concepts. It then makes use of the salient features of the metaphorically connected clusters to identify the actual metaphorical expressions. In contrast to previous work, our method is fully unsupervised. Despite this fact, it operates with an encouraging precision (0.69) and recall (0.61). Our approach is also the first one in NLP to exploit the cognitive findings on the differences in organisation of abstract and concrete concepts in the human brain. | Unsupervised Metaphor Identification Using Hierarchical Graph Factorization Clustering |
d14481277 | Speech technologies provide ways of helping people with hearing loss by improving their autonomy. This study focuses on an application in French language which is developed in the collaborative project RAPSODIE in order to improve communication between a hearing person and a deaf or hardof-hearing person. Our goal is to investigate different ways of displaying the speech recognition results which takes also into account the reliability of the recognized items. In this qualitative study, 10 persons have been interviewed to find the best way of displaying the speech transcription results. All the participants are deaf with different levels of hearing loss and various modes of communication. | Qualitative investigation of the display of speech recognition results for communication with deaf people |
d219328571 | THE FINITE STRING NEWSLETrER | |
d243865587 | This paper proposes a new representation for CCG derivations. CCG derivations are represented as trees whose nodes are labeled with categories strictly restricted by CCG rule schemata. This characteristic is not suitable for span-based parsing models because they predict node labels independently. In other words, span-based models may generate invalid CCG derivations that violate the rule schemata. Our proposed representation decomposes CCG derivations into several independent pieces and prevents the span-based parsing models from violating the schemata. Our experimental result shows that an off-theshelf span-based parser with our representation is comparable with previous CCG parsers. | A New Representation for Span-based CCG Parsing |
d231815627 | In open-domain question answering, dense passage retrieval has become a new paradigm to retrieve relevant passages for finding answers. Typically, the dual-encoder architecture is adopted to learn dense representations of questions and passages for semantic matching. However, it is difficult to effectively train a dual-encoder due to the challenges including the discrepancy between training and inference, the existence of unlabeled positives and limited training data. To address these challenges, we propose an optimized training approach, called RocketQA, to improving dense passage retrieval. We make three major technical contributions in RocketQA, namely crossbatch negatives, denoised hard negatives and data augmentation. The experiment results show that RocketQA significantly outperforms previous state-of-the-art models on both MS-MARCO and Natural Questions. We also conduct extensive experiments to examine the effectiveness of the three strategies in RocketQA. Besides, we demonstrate that the performance of end-to-end QA can be improved based on our RocketQA retriever 1 . | RocketQA: An Optimized Training Approach to Dense Passage Retrieval for Open-Domain Question Answering |
d219573654 | Automatic metrics are fundamental for the development and evaluation of machine translation systems. Judging whether, and to what extent, automatic metrics concur with the gold standard of human evaluation is not a straightforward problem. We show that current methods for judging metrics are highly sensitive to the translations used for assessment, particularly the presence of outliers, which often leads to falsely confident conclusions about a metric's efficacy. Finally, we turn to pairwise system ranking, developing a method for thresholding performance improvement under an automatic metric against human judgements, which allows quantification of type I versus type II errors incurred, i.e., insignificant human differences in system quality that are accepted, and significant human differences that are rejected. Together, these findings suggest improvements to the protocols for metric evaluation and system performance evaluation in machine translation. | Tangled up in BLEU: Reevaluating the Evaluation of Automatic Machine Translation Evaluation Metrics |
d6470935 | This paper presents the results of the WMT12 shared tasks, which included a translation task, a task for machine translation evaluation metrics, and a task for run-time estimation of machine translation quality. We conducted a large-scale manual evaluation of 103 machine translation systems submitted by 34 teams. We used the ranking of these systems to measure how strongly automatic metrics correlate with human judgments of translation quality for 12 evaluation metrics. We introduced a new quality estimation task this year, and evaluated submissions from 11 teams.12IDParticipant CMU Carnegie Mellon University (Denkowski et al., 2012) CU-BOJAR Charles University -Bojar (Bojar et al., 2012) CU-DEPFIX Charles University -DEPFIX (Rosa et al., 2012) CU-POOR-COMB Charles University -Bojar (Bojar et al., 2012) CU-TAMCH Charles University -Tamchyna (Tamchyna et al., 2012) CU-TECTOMT Charles University -TectoMT (Dušek et al., 2012) | Findings of the 2012 Workshop on Statistical Machine Translation |
d17878716 | This paper proposes a method of automatic transliteration from English to Japanese words. Our method successfully transliterates an English word not registered in any bilingual or pronunciation dictionaries by converting each partial letters in the English word into Japanese katakana characters. In such transliteration, identical letters occurring in different English words must often be converted into different katakana. To produce an adequate transliteration, the proposed method considers chunking of alphabetic letters of an English word into conversion units and considers English and Japanese context information simultaneously to calculate the plausibility of conversion. We have confirmed experimentally that the proposed method improves the conversion accuracy by 63% compared to a simple method that ignores the plausibility of chunking and contextual information.Conversion units based English to Japanese transliterationIn the case of transliteration to Japanese, special characters called katakana are used to indicate how a word is pronounced. For example, a transliteration into katakana of the word "actinium" is written "アクチニウム(a ku chi ni u mu)." (Here, the italics indicate Romanized katakana characters.) Handling an unknown word that does not registered in any dictionaries requires that the word must be divided into certain conversion units.Figure 1shows an example of conversion based English to Japanese transliteration. English: actinium a/c/ti/ni/u/m English conversion units: Converted Japanese units: ア/ク/チ/ニ/ウ/ム (a/ku/chi/ni/u/mu) Japanese: アクチニウム (a ku chi ni u mu) | Transliteration Considering Context Information based on the Maximum Entropy Method |
d199379584 | In this paper, we present a Dialect Identification system (ArbDialectID) that competed at Task 1 of the MADAR shared task, MADAR Travel Domain Dialect Identification. We build a coarse and a fine grained identification model to predict the label (corresponding to a dialect of Arabic) of a given text. We build two language models by extracting features at two levels (words and characters). We firstly build a coarse identification model to classify each sentence into one out of six dialects, then use this label as a feature for the fine grained model that classifies the sentence among 26 dialects from different Arab cities, after that we apply ensemble voting classifier on both subsystems. Our system ranked 1st that achieving an f-score of 67.32%. Both the models and our feature engineering tools are made available to the research community. | ArbDialectID at MADAR Shared Task 1: Language Modelling and Ensemble Learning for Fine Grained Arabic Dialect Identification |
d202565600 | Identifying what is at the center of the meaning of a word and what discriminates it from other words is a fundamental natural language inference task. This paper describes an explicit word vector representation model (WVM) to support the identification of discriminative attributes. A core contribution of the paper is a quantitative and qualitative comparative analysis of different types of data sources and Knowledge Bases in the construction of explainable and explicit WVMs: (i) knowledge graphs built from dictionary definitions, (ii) entity-attribute-relationships graphs derived from images and (iii) commonsense knowledge graphs. Using a detailed quantitative and qualitative analysis, we demonstrate that these data sources have complementary semantic aspects, supporting the creation of explicit semantic vector spaces. The explicit vector spaces are evaluated using the task of discriminative attribute identification, showing comparable performance to the state-ofthe-art systems in the task (F1-score = 0.69), while delivering full model transparency and explainability. | Identifying and Explaining Discriminative Attributes |
d1418199 | Semantic Role Labeling (SRL) as a Shallow Semantic Parsing causes more and more attention recently. The shortage of manually tagged data is one of main obstacles to supervised learning, which is even serious in SRL. Transductive SVM (TSVM) is a novel semi-supervised learning method special to small mount of tagged data. In this paper, we introduce an application of TSVM in Chinese SRL. To improve the performance of TSVM, some heuristics have been designed from the semantic perspective. The experiment results on Chinese Propbank showed that TSVM outperforms SVM in small tagged data, and after using heuristics, it performs further better. | Semantic Role Labeling of Chinese Using Transductive SVM and Semantic Heuristics |
d1428364 | The task of identifying the language in which a given document (ranging from a sentence to thousands of pages) is written has been relatively well studied over several decades. Automated approaches to written language identification are used widely throughout research and industrial contexts, over both oral and written source materials. Despite this widespread acceptance, a review of previous research in written language identification reveals a number of questions which remain open and ripe for further investigation. | Reconsidering Language Identification for Written Language Resources |
d219302339 | Structured prediction is one of the most important topics in various fields, including machine learning, computer vision, natural language processing (NLP) and bioinformatics. In this tutorial, we present a novel framework that unifies various structured prediction models. The hidden Markov model (HMM) and the probabilistic context-free grammars (PCFGs) are two classic generative models used for predicting outputs with linear-chain and tree structures, respectively. As HMM's discriminative counterpart, the linear-chain conditional random fields (CRFs)(Lafferty et al., 2001)model was later proposed. Such a model was shown to yield good performance on standard NLP tasks such as information extraction. Several extensions to such a model were then proposed afterward, including the semi-Markov CRFs (Sarawagi and Cohen, 2004), tree CRFs (Cohn and Blunsom, 2005), as well as discriminative parsing models and their latent variable variants(Petrov and Klein, 2007). On the other hand, utilizing a slightly different loss function, one could arrive at the structured support vector machines(Tsochantaridis et al., 2004)and its latent variable variant (Yu and Joachims, 2009) as well. Furthermore, new models that integrate neural networks and graphical models, such as neural CRFs(Do et al., 2010)were also proposed.In this tutorial, we will be discussing how such a wide spectrum of existing structured prediction models can all be implemented under a unified framework 1 that involves some basic building blocks. Based on such a framework, we show how some seemingly complicated structured prediction models such as a semantic parsing model(Lu et al., 2008;Lu, 2014)can be implemented conveniently and quickly. Furthermore, we also show that the framework can be used to solve certain structured prediction problems that otherwise cannot be easily handled by conventional structured 1 http://statnlp.org/statnlp-framework prediction models. Specifically, we show how to use such a framework to construct models that are capable of predicting non-conventional structures, such as overlapping structures(Lu and Roth, 2015;Muis and Lu, 2016a). We will also discuss how to make use of the framework to build other related models such as topic models and highlight its potential applications in some recent popular tasks (e.g., AMR parsing(Flanigan et al., 2014)).This tutorial consists of the following 3 main sections. 2Foundations of structured prediction modelsDuration: 45 minutesIn this section, we introduce the basics of structured prediction models. We will review all the above-mentioned structured prediction models. We then provide a global picture that shows the underlying connections between different models.Unified framework for structured prediction Duration: 45 minutesIn this section, we formally introduce the framework that allows all such different structured prediction models to be unified in an elegant manner. We start with defining the basic building blocks required for constructing a structured prediction model. Next, we discuss how to make use of such building blocks for constructing different types of models.Practical guide on model implementation Duration: 60 minutesIn this section, we present a practical guide on how to implement seemingly very different types of structured prediction models using our unified framework. We will show 2 The material associated with this tutorial will be available at http://statnlp.org/tutorials/. | A Unified Framework for Structured Prediction: From Theory to Practice |
d21780723 | This work aims at resolving coreference in Portuguese, focusing on categories of named entities Person, Location and Organization. The proposed method uses supervised learning. To this end, the use of features that assist in the correct classification of named entities is critical. The construction and refinement of these features are of great relevance to his task. The performance of many other tasks depends on the correct output of coreference resolution systems, in special the extraction of relationships between named entities. | Geração de features para resolução de correferência: Pessoa, Local e Organização |
d221836121 | Training a supervised neural network classifier typically requires many annotated training samples. Collecting and annotating a large number of data points are costly and sometimes even infeasible. Traditional annotation process uses a low-bandwidth human-machine communication interface: classification labels, each of which only provides a few bits of information. We propose Active Learning with Contrastive Explanations (ALICE), an expert-in-the-loop training framework that utilizes contrastive natural language explanations to improve data efficiency in learning. AL-ICE learns to first use active learning to select the most informative pairs of label classes to elicit contrastive natural language explanations from experts. Then it extracts knowledge from these explanations using a semantic parser. Finally, it incorporates the extracted knowledge through dynamically changing the learning model's structure. We applied ALICE in two visual recognition tasks, bird species classification and social relationship classification. We found by incorporating contrastive explanations, our models outperform baseline models that are trained with 40-100% more training data. We found that adding 1 explanation leads to similar performance gain as adding 13-30 labeled training data points. | ALICE: Active Learning with Contrastive Natural Language Explanations |
d195316274 | In practical scenario, relation extraction needs to first identify entity pairs that have relation and then assign a correct relation class. However, the number of non-relation entity pairs in context (negative instances) usually far exceeds the others (positive instances), which negatively affects a model's performance. To mitigate this problem, we propose a multitask architecture which jointly trains a model to perform relation identification with crossentropy loss and relation classification with ranking loss. Meanwhile, we observe that a sentence may have multiple entities and relation mentions, and the patterns in which the entities appear in a sentence may contain useful semantic information that can be utilized to distinguish between positive and negative instances. Thus we further incorporate the embeddings of character-wise/word-wise BIO tag from the named entity recognition task into character/word embeddings to enrich the input representation. Experiment results show that our proposed approach can significantly improve the performance of a baseline model with more than 10% absolute increase in F1-score, and outperform the state-of-theart models on ACE 2005 Chinese and English corpus. Moreover, BIO tag embeddings are particularly effective and can be used to improve other models as well. | Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data |
d49358911 | We study three general multi-task learning (MTL) approaches on 11 sequence tagging tasks. Our extensive empirical results show that in about 50% of the cases, jointly learning all 11 tasks improves upon either independent or pairwise learning of the tasks. We also show that pairwise MTL can inform us what tasks can benefit others or what tasks can be benefited if they are learned jointly. In particular, we identify tasks that can always benefit others as well as tasks that can always be harmed by others. Interestingly, one of our MTL approaches yields embeddings of the tasks that reveal the natural clustering of semantic and syntactic tasks. Our inquiries have opened the doors to further utilization of MTL in NLP.This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/ | Multi-Task Learning for Sequence Tagging: An Empirical Study |
d213929104 | Existing open-domain dialogue generation models are usually trained to mimic the gold response in the training set using cross-entropy loss on the vocabulary. However, a good response does not need to resemble the gold response, since there are multiple possible responses to a given prompt. In this work, we hypothesize that the current models are unable to integrate information from multiple semantically similar valid responses of a prompt, resulting in the generation of generic and uninformative responses. To address this issue, we propose an alternative to the end-to-end classification on vocabulary. We learn the pair relationship between the prompts and responses as a regression task on a latent space instead. In our novel dialog generation model, the representations of semantically related sentences are close to each other on the latent space. Human evaluation showed that learning the task on a continuous space can generate responses that are both relevant and informative. | Generating Dialogue Responses from a Semantic Latent Space |
d189927857 | This paper is concerned with the task of multi-hop open-domain Question Answering (QA). This task is particularly challenging since it requires the simultaneous performance of textual reasoning and efficient searching. We present a method for retrieving multiple supporting paragraphs, nested amidst a large knowledge base, which contain the necessary evidence to answer a given question. Our method iteratively retrieves supporting paragraphs by forming a joint vector representation of both a question and a paragraph. The retrieval is performed by considering contextualized sentence-level representations of the paragraphs in the knowledge source. Our method achieves state-of-the-art performance over two well-known datasets, SQuAD-Open and HotpotQA, which serve as our single-and multi-hop open-domain QA benchmarks, respectively. 1 | Multi-Hop Paragraph Retrieval for Open-Domain Question Answering |
d257767694 | Verb Phrase Anaphora (VPA) is a universal language phenomenon. It can occur in the form of do so phrase, verb phrase ellipsis, etc. Resolving VPA can improve the performance of Dialogue processing systems, Natural Language Generation (NLG), Question Answering (QA) and so on. In this paper, we present a novel computational approach to resolve the specific verb phrase anaphora appearing as do so construct and its lexical variations for the English language. The approach follows a heuristic technique using a combination of parsing from classical NLP, state-of-the-art (SOTA) Generative Pre-trained Transformer (GPT) language model and RoBERTa grammar correction model. The result indicates that our approach can resolve these specific verb phrase anaphora cases with 73.40 F1 score. The data set used for testing the specific verb phrase anaphora cases of do so and doing so is released for research purposes. This module has been used as the last module in a coreference resolution pipeline for a downstream QA task for the electronic home appliances sector. | Verb Phrase Anaphora: Do(ing) so with Heuristics |
d236486069 | Pretrained language models have served as the backbone for many state-of-the-art NLP results. These models are large and expensive to train. Recent work suggests that continued pretraining on task-specific data is worth the effort as pretraining leads to improved performance on downstream tasks. We explore alternatives to full-scale task-specific pretraining of language models through the use of adapter modules, a parameter-efficient approach to transfer learning. We find that adapter-based pretraining is able to achieve comparable results to task-specific pretraining while using a fraction of the overall trainable parameters. We further explore direct use of adapters without pretraining and find that the direct finetuning performs mostly on par with pretrained adapter models, contradicting previously proposed benefits of continual pretraining in full pretraining fine-tuning strategies. Lastly, we perform an ablation study on task-adaptive pretraining to investigate how different hyperparameter settings can change the effectiveness of the pretraining. | Revisiting Pretraining with Adapters |
d202766615 | Semantic role labeling (SRL) aims to identify the predicate-argument structure of a sentence. Inspired by the strong correlation between syntax and semantics, previous works pay much attention to improve SRL performance on exploiting syntactic knowledge, achieving significant results. Pipeline methods based on automatic syntactic trees and multi-task learning (MTL) approaches using standard syntactic trees are two common research orientations. In this paper, we adopt a simple unified span-based model for both span-based and word-based Chinese SRL as a strong baseline. Besides, we present a MTL framework that includes the basic SRL module and a dependency parser module. Different from the commonly used hard parameter sharing strategy in MTL, the main idea is to extract implicit syntactic representations from the dependency parser as external inputs for the basic SRL model. Experiments on the benchmarks of Chinese Proposition Bank 1.0 and CoNLL-2009 Chinese datasets show that our proposed framework can effectively improve the performance over the strong baselines. With the external BERT representations, our framework achieves new state-of-the-art 87.54 and 88.5 F1 scores on the two test data of the two benchmarks, respectively. In-depth analysis are conducted to gain more insights on the proposed framework and the effectiveness of syntax. | A Syntax-aware Multi-task Learning Framework for Chinese Semantic Role Labeling |
d221739231 | Despite the success of existing referenced metrics (e.g., BLEU and MoverScore), they correlate poorly with human judgments for openended text generation including story or dialog generation because of the notorious oneto-many issue: there are many plausible outputs for the same input, which may differ substantially in literal or semantics from the limited number of given references. To alleviate this issue, we propose UNION, a learnable UNreferenced metrIc for evaluating Open-eNded story generation, which measures the quality of a generated story without any reference. Built on top of BERT, UNION is trained to distinguish human-written stories from negative samples and recover the perturbation in negative stories. We propose an approach of constructing negative samples by mimicking the errors commonly observed in existing NLG models, including repeated plots, conflicting logic, and long-range incoherence. Experiments on two story datasets demonstrate that UNION is a reliable measure for evaluating the quality of generated stories, which correlates better with human judgments and is more generalizable than existing state-of-theart metrics. | UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation |
d7126036 | We describe a machine learning approach for the 2017 shared task on Native Language Identification (NLI). The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based on character p-grams (also known as n-grams) extracted from essays or speech transcripts, we also use a kernel based on i-vectors, a low-dimensional representation of audio recordings, provided by the shared task organizers. For the learning stage, we choose Kernel Discriminant Analysis (KDA) over Kernel Ridge Regression (KRR), because the former classifier obtains better results than the latter one on the development set. In our previous work, we have used a similar machine learning approach to achieve stateof-the-art NLI results. The goal of this paper is to demonstrate that our shallow and simple approach based on string kernels (with minor improvements) can pass the test of time and reach state-of-theart performance in the 2017 NLI shared task, despite the recent advances in natural language processing. We participated in all three tracks, in which the competitors were allowed to use only the essays (essay track), only the speech transcripts (speech track), or both (fusion track). Using only the data provided by the organizers for training our models, we have reached a macro F 1 score of 86.95% in the closed essay track, a macro F 1 score of 87.55% in the closed speech track, and a macro F 1 score of 93.19% in the closed * The authors have equally contributed to this work. fusion track. With these scores, our team (UnibucKernel) ranked in the first group of teams in all three tracks, while attaining the best scores in the speech and the fusion tracks. | Can string kernels pass the test of time in Native Language Identification? |
d204744131 | Cross-lingual dependency parsing involves transferring syntactic knowledge from one language to another. It is a crucial component for inducing dependency parsers in low-resource scenarios where no training data for a language exists. Using Faroese as the target language, we compare two approaches using annotation projection: first, projecting from multiple monolingual source models; second, projecting from a single polyglot model which is trained on the combination of all source languages. Furthermore, we reproduce multisource projection(Tyers et al., 2018), in which dependency trees of multiple sources are combined. Finally, we apply multi-treebank modelling to the projected treebanks, in addition to or alternatively to polyglot modelling on the source side. We find that polyglot training on the source languages produces an overall trend of better results on the target language but the single best result for the target language is obtained by projecting from monolingual source parsing models and then training multi-treebank POS tagging and parsing models on the target side. | Cross-lingual Parsing with Polyglot Training and Multi-treebank Learning: A Faroese Case Study |
d196189186 | Table-to-text generation aims to translate the structured data into the unstructured text. Most existing methods adopt the encoder-decoder framework to learn the transformation, which requires large-scale training samples. However, the lack of large parallel data is a major practical problem for many domains. In this work, we consider the scenario of low resource table-to-text generation, where only limited parallel data is available. We propose a novel model to separate the generation into two stages: key fact prediction and surface realization. It first predicts the key facts from the tables, and then generates the text with the key facts. The training of key fact prediction needs much fewer annotated data, while surface realization can be trained with pseudo parallel corpus. We evaluate our model on a biography generation dataset. Our model can achieve 27.34 BLEU score with only 1, 000 parallel data, while the baseline model only obtain the performance of 9.71 BLEU score. 1 | Key Fact as Pivot: A Two-Stage Model for Low Resource Table-to-Text Generation |
d218487628 | The noun lexica of many natural languages are divided into several declension classes with characteristic morphological properties. Class membership is far from deterministic, but the phonological form of a noun and its meaning can often provide imperfect clues. Here, we investigate the strength of those clues. More specifically, we operationalize "strength" as measuring how much information, in bits, we can glean about declension class from knowing the form and meaning of nouns. We know that form and meaning are often also indicative of grammatical gender-which, as we quantitatively verify, can itself share information with declension class-so we also control for gender. We find for two Indo-European languages (Czech and German) that form and meaning share a significant amount of information with class (and contribute additional information beyond gender). The three-way interaction between class, form, and meaning (given gender) is also significant. Our study is important for two reasons: First, we introduce a new method that provides additional quantitative support for a classic linguistic finding that form and meaning are relevant for the classification of nouns into declensions. Second, we show not only that individual declension classes vary in the strength of their clues within a language, but also that the variations between classes vary across languages. The code is publicly available at https://github.com/ rycolab/declension-mi. | Predicting Declension Class from Form and Meaning |
d218862840 | Neural sequence labelling approaches have achieved state of the art results in morphological tagging. We evaluate the efficacy of four standard sequence labelling models on Sanskrit, a morphologically rich, fusional Indian language. As its label space can theoretically contain more than 40,000 labels, systems that explicitly model the internal structure of a label are more suited for the task, because of their ability to generalise to labels not seen during training. We find that although some neural models perform better than others, one of the common causes for error for all of these models is mispredictions due to syncretism. 1 | Evaluating Neural Morphological Taggers for Sanskrit |
d248721928 | This paper investigates the effectiveness of sentence-level transformers for zero-shot offensive span identification on a code-mixed Tamil dataset. More specifically, we evaluate rationale extraction methods of Local Interpretable Model Agnostic Explanations (LIME)(Ribeiro et al., 2016a)and Integrated Gradients (IG) (Sundararajan et al., 2017) for adapting transformer based offensive language classification models for zero-shot offensive span identification. To this end, we find that LIME and IG show baseline F 1 of 26.35% and 44.83%, respectively. Besides, we study the effect of data set size and training process on the overall accuracy of span identification. As a result, we find both LIME and IG to show significant improvement with Masked Data Augmentation and Multilabel Training, with F 1 of 50.23% and 47.38% respectively. Disclaimer : This paper contains examples that may be considered profane, vulgar, or offensive. The examples do not represent the views of the authors or their employers/graduate schools towards any person(s), group(s), practice(s), or entity/entities. Instead they are used to emphasize only the linguistic research challenges. | Zero-shot Code-Mixed Offensive Span Identification through Rationale Extraction |
d171820989 | Neural Machine Translation | |
d218665670 | There is a growing interest in developing goaloriented dialog systems which serve users in accomplishing complex tasks through multiturn conversations. Although many methods are devised to evaluate and improve the performance of individual dialog components, there is a lack of comprehensive empirical study on how different components contribute to the overall performance of a dialog system. In this paper, we perform a system-wise evaluation and present an empirical analysis on different types of dialog systems which are composed of different modules in different settings. Our results show that (1) a pipeline dialog system trained using fine-grained supervision signals at different component levels often obtains better performance than the systems that use joint or end-to-end models trained on coarse-grained labels, (2) component-wise, single-turn evaluation results are not always consistent with the overall performance of a dialog system, and (3) despite the discrepancy between simulators and human users, simulated evaluation is still a valid alternative to the costly human evaluation especially in the early stage of development. * Corresponding author End-to-End Word-Level Policy Word-Level DST NLU DST Policy NLG State Semantic Info Action DatabaseFigure 1: Different architectures of goal-oriented dialog systems. It can be constructed as a pipeline or endto-end system with different granularity. | Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation |
d49534653 | Annotated corpora enable supervised machine learning and data analysis. To reduce the cost of manual annotation, tasks are often assigned to internet workers whose judgments are reconciled by crowdsourcing models. We approach the problem of crowdsourcing using a framework for learning from rich prior knowledge, and we identify a family of crowdsourcing models with the novel ability to combine annotations with differing structures: e.g., document labels and word labels. Annotator judgments are given in the form of the predicted expected value of measurement functions computed over annotations and the data, unifying annotation models. Our model, a specific instance of this framework, compares favorably with previous work. Furthermore, it enables active sample selection, jointly selecting annotator, data item, and annotation structure to reduce annotation effort.Annotierte Korpora ermöglichenüberwachtes maschinelles Lernen und Datenanalyse. Um die Kosten für manuelle Annotationen zu vermeiden, werden Aufgaben häufig Internetarbeitern zugewiesen, deren Urteile durch Crowdsourcing-Modelle abgeglichen werden. Wir nähern uns dem Problem des Crowdsourcings, indem wir einen Rahmen für das Lernen aus reichem Vorwissen vorschlagen, und wir bestimmen eine Familie von Crowdsourcing-Modellen mit der Fähigkeit, Annotationen mit unterschiedlichen Strukturen zu kombinieren: z.B., Dokumentbezeichnungen und Wortbezeichnungen. Bewertungen werden in Form des vorhergesagten erwarteten Werts von Messfunktionen (measurement functions) gegeben, dieüber Annotationen und die Daten berechnet werden. Darin werden die vorherige Annotationsmodelle vereinheitlicht. Unser Modell, eine spezifische Instanz dieses Rahmens, schneidet im Vergleich zu früheren Arbeiten positiv ab. Darüber hinaus ermöglicht es die aktive Stichprobenauswahl, indem Kommentator, Datenelement, und Annotationsstruktur gemeinsam ausgewählt werden, um den Annotationskosten zu reduzieren. | Learning from Measurements in Crowdsourcing Models: Inferring Ground Truth from Diverse Annotation Types |
d243865611 | The availability of corpora has led to significant advances in training semantic parsers in English. Unfortunately, for languages other than English, annotated data is limited and so is the performance of the developed parsers. Recently, pretrained multilingual models have been proven useful for zero-shot cross-lingual transfer in many NLP tasks. What else does it require to apply a parser trained in English to other languages for zero-shot cross-lingual semantic parsing? Will simple language-independent features help? To this end, we experiment with six Discourse Representation Structure (DRS) semantic parsers in English, and generalize them to Italian, German and Dutch, where there are only a small number of manually annotated parses available. Extensive experiments show that despite its simplicity, adding Universal Dependency (UD) relations and Universal POS tags (UPOS) as model-agnostic features achieves surprisingly strong improvement on all parsers. We have publicly released our code at https://github.com/GT-SALT/ Multilingual-DRS-Semantic-Parsing . | Frustratingly Simple but Surprisingly Strong: Using Language-Independent Features for Zero-shot Cross-lingual Semantic Parsing |
d5674153 | Translated bi-texts contain complementary language cues, and previous work on Named Entity Recognition (NER) has demonstrated improvements in performance over monolingual taggers by promoting agreement of tagging decisions between the two languages. However, most previous approaches to bilingual tagging assume word alignments are given as fixed input, which can cause cascading errors. We observe that NER label information can be used to correct alignment mistakes, and present a graphical model that performs bilingual NER tagging jointly with word alignment, by combining two monolingual tagging models with two unidirectional alignment models. We introduce additional cross-lingual edge factors that encourage agreements between tagging and alignment decisions. We design a dual decomposition inference algorithm to perform joint decoding over the combined alignment and NER output space. Experiments on the OntoNotes dataset demonstrate that our method yields significant improvements in both NER and word alignment over state-of-the-art monolingual baselines. | Joint Word Alignment and Bilingual Named Entity Recognition Using Dual Decomposition |
d70790 | The ReachOut clinical psychology shared task challenge addresses the problem of providing an automatic triage for posts to a support forum for people with a history of mental health issues. Posts are classified into green, amber, red and crisis. The non-green categories correspond to increasing levels of urgency for some form of intervention. The Thomson Reuters submissions arose from an idea about self-training and ensemble learning. The available labeled training set is small (947 examples) and the class distribution unbalanced. It was therefore hoped to develop a method that would make use of the larger dataset of unlabeled posts provided by the organisers. This did not work, but the performance of a radial basis function SVM intended as a baseline was relatively good. Therefore, the report focuses on the latter, aiming to understand the reasons for its performance. | Classifying ReachOut posts with a radial basis function SVM |
d51977305 | We describe an annotation scheme and a tool developed for creating linguistically annotated corpora for non-configurational languages. Since the requirements for such a formalism differ from those posited for configurational languages, several features have been added, influencing the architecture of the scheme. The resulting scheme reflects a stratificational notion of language, and makes only minimal assumptions about the interrelation of the particu-Jar representational strata. | An Annotation Scheme for Free Word Order Languages |
d159040684 | We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper. Turing Tests, where a biomedical domain expert is asked to compare a system output and a human-authored string, show PaperRobot generated abstracts, conclusion and future work sections, and new titles are chosen over human-written ones up to 30%, 24% and 12% of the time, respectively. 1 | PaperRobot: Incremental Draft Generation of Scientific Ideas |
d184486746 | Large pre-trained neural networks such as BERT have had great recent success in NLP, motivating a growing body of research investigating what aspects of language they are able to learn from unlabeled data. Most recent analysis has focused on model outputs (e.g., language model surprisal) or internal vector representations (e.g., probing classifiers). Complementary to these works, we propose methods for analyzing the attention mechanisms of pre-trained models and apply them to BERT. BERT's attention heads exhibit patterns such as attending to delimiter tokens, specific positional offsets, or broadly attending over the whole sentence, with heads in the same layer often exhibiting similar behaviors. We further show that certain attention heads correspond well to linguistic notions of syntax and coreference. For example, we find heads that attend to the direct objects of verbs, determiners of nouns, objects of prepositions, and coreferent mentions with remarkably high accuracy. Lastly, we propose an attention-based probing classifier and use it to further demonstrate that substantial syntactic information is captured in BERT's attention.1 Code will be released at https://github.com/ clarkkev/attention-analysis. 2 We use the English base-sized model. | What Does BERT Look At? An Analysis of BERT's Attention |
d236486206 | The paper reports on a corpus study of German light verb constructions (LVCs). LVCs come in families which exemplify systematic interpretation patterns. The paper's aim is to account for the properties determining these patterns on the basis of a corpus study on German LVCs of the type 'stehen unter NP' ('stand under NP'). | Light Verb Constructions and Their Families -A Corpus Study on German stehen unter-LVCs |
d13612329 | This paper describes a tool developed to improve access to the enormous volume of data housed at the UK's National Archives, both for the general public and for specialist researchers. The system we have developed, TNA-Search, enables a multi-paradigm search over the entire electronic archive (42TB of data in various formats). The search functionality allows queries that arbitrarily mix any combination of full-text, structural, linguistic and semantic queries. The archive is annotated and indexed with respect to a massive semantic knowledge base containing data from the LOD cloud, data.gov.uk, related TNA projects, and a large geographical database. The semantic annotation component achieves approximately 83% F-measure, which is very reasonable considering the wide range of entities and document types and the open domain. The technologies are being adopted by real users at The National Archives and will form the core of their suite of search tools, with additional in-house interfaces. | Large Scale Semantic Annotation, Indexing, and Search at The National Archives |
d218883429 | Opinion summarization is the task of automatically creating summaries that reflect subjective information expressed in multiple documents, such as product reviews. While the majority of previous work has focused on the extractive setting, i.e., selecting fragments from input reviews to produce a summary, we let the model generate novel sentences and hence produce abstractive summaries. Recent progress in summarization has seen the development of supervised models which rely on large quantities of document-summary pairs. Since such training data is expensive to acquire, we instead consider the unsupervised setting, in other words, we do not use any summaries in training. We define a generative model for a review collection which capitalizes on the intuition that when generating a new review given a set of other reviews of a product, we should be able to control the "amount of novelty" going into the new review or, equivalently, vary the extent to which it deviates from the input. At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions. We capture this intuition by defining a hierarchical variational autoencoder model. Both individual reviews and the products they correspond to are associated with stochastic latent codes, and the review generator ("decoder") has direct access to the text of input reviews through the pointergenerator mechanism. Experiments on Amazon and Yelp datasets, show that setting at test time the review's latent code to its mean, allows the model to produce fluent and coherent summaries reflecting common opinions. | Unsupervised Opinion Summarization as Copycat-Review Generation |
d218613673 | Deciding which scripts to turn into movies is a costly and time-consuming process for filmmakers. Thus, building a tool to aid script selection, an initial phase in movie production, can be very beneficial. Toward that goal, in this work, we present a method to evaluate the quality of a screenplay based on linguistic cues. We address this in a two-fold approach: (1) we define the task as predicting nominations of scripts at major film awards with the hypothesis that the peer-recognized scripts should have a greater chance to succeed.(2) based on industry opinions and narratology, we extract and integrate domain-specific features into common classification techniques. We face two challenges (1) scripts are much longer than other document datasets (2) nominated scripts are limited and thus difficult to collect. However, with narratology-inspired modeling and domain features, our approach offers clear improvements over strong baselines. Our work provides a new approach for future work in screenplay analysis. | Screenplay Quality Assessment: Can We Predict Who Gets Nominated? |
d174800557 | Training data for text classification is often limited in practice, especially for applications with many output classes or involving many related classification problems. This means classifiers must generalize from limited evidence, but the manner and extent of generalization is task dependent. Current practice primarily relies on pre-trained word embeddings to map words unseen in training to similar seen ones. Unfortunately, this squishes many components of meaning into highly restricted capacity. Our alternative begins with sparse pre-trained representations derived from unlabeled parsed corpora; based on the available training data, we select features that offers the relevant generalizations. This produces taskspecific semantic vectors; here, we show that a feed-forward network over these vectors is especially effective in low-data scenarios, compared to existing state-of-the-art methods. By further pairing this network with a convolutional neural network, we keep this edge in low data scenarios and remain competitive when using full training sets. | Text Classification with Few Examples using Controlled Generalization |
d174801285 | Although neural conversation models are effective in learning how to produce fluent responses, their primary challenge lies in knowing what to say to make the conversation contentful and non-vacuous. We present a new end-to-end approach to contentful neural conversation that jointly models response generation and on-demand machine reading. The key idea is to provide the conversation model with relevant long-form text on the fly as a source of external knowledge. The model performs QA-style reading comprehension on this text in response to each conversational turn, thereby allowing for more focused integration of external knowledge than has been possible in prior approaches. To support further research on knowledge-grounded conversation, we introduce a new large-scale conversation dataset grounded in external web pages (2.8M turns, 7.4M sentences of grounding). Both human evaluation and automated metrics show that our approach results in more contentful responses compared to a variety of previous methods, improving both the informativeness and diversity of generated output. | Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading |
d218487151 | Transformer-based QA models use input-wide self-attention -i.e. across both the question and the input passage -at all layers, causing them to be slow and memory-intensive. It turns out that we can get by without inputwide self-attention at all layers, especially in the lower layers. We introduce DeFormer, a decomposed transformer, which substitutes the full self-attention with question-wide and passage-wide self-attentions in the lower layers. This allows for question-independent processing of the input text representations, which in turn enables pre-computing passage representations reducing runtime compute drastically. Furthermore, because DeFormer is largely similar to the original model, we can initialize DeFormer with the pre-training weights of a standard transformer, and directly fine-tune on the target QA dataset. We show DeFormer versions of BERT and XLNet can be used to speed up QA by over 4.3x and with simple distillation-based losses they incur only a 1% drop in accuracy. We open source the code at https://github.com/ StonyBrookNLP/deformer. | DeFormer: Decomposing Pre-trained Transformers for Faster Question Answering |
d218674375 | Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers. | A Scientific Information Extraction Dataset for Nature Inspired Engineering |
d222310559 | Language models (LMs) have proven surprisingly successful at capturing factual knowledge by completing cloze-style fill-in-theblank questions such as "Punta Cana is located in _." However, while knowledge is both written and queried in many languages, studies on LMs' factual representation ability have almost invariably been performed on English. To assess factual knowledge retrieval in LMs in different languages, we create a multilingual benchmark of cloze-style probes for 23 typologically diverse languages. To properly handle language variations, we expand probing methods from single-to multi-word entities, and develop several decoding algorithms to generate multi-token predictions. Extensive experimental results provide insights about how well (or poorly) current state-of-theart LMs perform at this task in languages with more or fewer available resources. We further propose a code-switching-based method to improve the ability of multilingual LMs to access knowledge, and verify its effectiveness on several benchmark languages. Benchmark data and code have been released at https://x-factr. github.io. | X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models |
d235377391 | Recent advances in Unsupervised Neural Machine Translation (UNMT) have minimized the gap between supervised and unsupervised machine translation performance for closely related language-pairs. However, the situation is very different for distant language pairs. Lack of lexical overlap and low syntactic similarities such as between English and Indo-Aryan languages lead to poor translation quality in existing UNMT systems. In this paper, we show that initialising the embedding layer of UNMT models with cross-lingual embeddings shows significant improvements in BLEU score over existing approaches with embeddings randomly initialized. Further, static embeddings (freezing the embedding layer weights) lead to better gains compared to updating the embedding layer weights during training (non-static). We experimented using Masked Sequence to Sequence (MASS) and Denoising Autoencoder (DAE) UNMT approaches for three distant language pairs. The proposed cross-lingual embedding initialization yields BLEU score improvement of as much as ten times over the baseline for English-Hindi, English-Bengali, and English-Gujarati. Our analysis shows the importance of cross-lingual embedding, comparisons between approaches, and the scope of improvements in these systems. | Crosslingual Embeddings are Essential in UNMT for Distant Languages: An English to IndoAryan Case Study |
d8267681 | We present an annotation scheme for information status (IS) in dialogue, and validate it on three Switchboard dialogues. We show that our scheme has good reproducibility, and compare it with previous attempts to code IS and related features. We eventually apply the scheme to 147 dialogues, thus producing a corpus that contains nearly 70,000 NPs annotated for IS and over 15,000 coreference links. | An annotation scheme for information status in dialogue * |
d237372057 | We tackle the problem of weakly-supervised conversational Question Answering over large Knowledge Graphs using a neural semantic parsing approach. We introduce a new Logical Form (LF) grammar that can model a wide range of queries on the graph while remaining sufficiently simple to generate supervision data efficiently. Our Transformer-based model takes a JSON-like structure as input, allowing us to easily incorporate both Knowledge Graph and conversational contexts. This structured input is transformed to lists of embeddings and then fed to standard attention layers. We validate our approach, both in terms of grammar coverage and LF execution accuracy, on two publicly available datasets, CSQA and ConvQuestions, both grounded in Wikidata. On CSQA, our approach increases the coverage from 80% to 96.2%, and the LF execution accuracy from 70.6% to 75.6%, with respect to previous state-of-the-art results. On ConvQuestions, we achieve competitive results with respect to the state-of-the-art. | Structured Context and High-Coverage Grammar for Conversational Question Answering over Knowledge Graphs |
d232021807 | Nous décrivons ici comment enrichir automatiquement WordNet en y important des articles encyclopédiques. Ce processus permet de créer des nouvelles entrées, en les rattachant au bon hyperonyme. Par ailleurs, les entrées préexistantes de WordNet peuvent être enrichies de descriptions complémentaires. La répétition de ce processus sur plusieurs encyclopédies permet de constituer un corpus d'articles comparables. On peut ensuite extraire automatiquement des paraphrases à partir des couples d'articles ainsi créés. Grâce à l'application d'une mesure de similarité, utilisant la hiérarchie de verbes de WordNet, les constituants de ces paraphrases peuvent être désambiguïsés.Abstract. We describe here how to automatically import encyclopedic articles intoWordNet. This process makes it possible to create new entries, attached to their appropriate hypernym. In addition, the preexisting entries of WordNet can get enriched with complementary descriptions. Reiterating this process on several encyclopedias makes it possible to constitute a corpus of comparable articles; we can then automatically extract paraphrases from the couples of articles that have been created. The paraphrases components can finally be disambiguated, by means of a similarity measure (using the verbs WordNet hierarchy). | Extraction de paraphrases désambiguïsées à partir d'un corpus d'articles encyclopédiques alignés automatiquement |
d201637843 | Translation systems aim to perform a meaningpreserving conversion of linguistic material (typically text but also speech) from a source to a target language (and, to a lesser degree, the corresponding socio-cultural contexts). Dubbing, i. e., the lip-synchronous translation and revoicing of speech adds to this constraints about the close matching of phonetic and resulting visemic synchrony characteristics of source and target material. There is an inherent conflict between a translation's meaning preservation and its 'dubbability' and the resulting trade-off can be controlled by weighing the synchrony constraints. We introduce our work, which to the best of our knowledge is the first of its kind, on integrating synchrony constraints into the machine translation paradigm. We present first results for the integration of synchrony constraints into encoder decoder-based neural machine translation and show that considerably more 'dubbable' translations can be achieved with only a small impact on BLEU score, and dubbability improves more steeply than BLEU degrades. | Integration of Dubbing Constraints into Machine Translation |
d201703375 | Recent concerns over abusive behavior on their platforms have pressured social media companies to strengthen their content moderation policies. However, user opinions on these policies have been relatively understudied. In this paper, we present an analysis of user responses to a September 27, 2018 announcement about the quarantine policy on Reddit as a case study of to what extent the discourse on content moderation is polarized by users' ideological viewpoint. We introduce a novel partitioning approach for characterizing user polarization based on their distribution of participation across interest subreddits. We then use automated techniques for capturing framing to examine how users with different viewpoints discuss moderation issues, finding that right-leaning users invoked censorship while left-leaning users highlighted inconsistencies on how content policies are applied. Overall, we argue for a more nuanced approach to moderation by highlighting the intersection of behavior and ideology in considering how abusive language is defined and regulated. 1 https://www.reddit.com/r/ announcements/comments/9jf8nh/ | The Discourse of Online Content Moderation: Investigating Polarized User Responses to Changes in Reddit's Quarantine Policy |
d243865648 | There is a growing consensus that surface form alone does not enable models to learn meaning and gain language understanding. This warrants an interest in hybrid systems that combine the strengths of neural and symbolic methods. We favour triadic systems consisting of neural networks, knowledge bases, and inference engines. The network provides perception, that is, the interface between the system and its environment. The knowledge base provides explicit memory and thus immediate access to established facts. Finally, inference capabilities are provided by the inference engine which reflects on the perception, supported by memory, to reason and discover new facts. In this work, we probe six popular language models for semantic relations and outline a future line of research to study how the constituent subsystems can be jointly realised and integrated. | Bridging Perception, Memory, and Inference through Semantic Relations * |
d215238846 | Complex reasoning over text requires understanding and chaining together free-form predicates and logical connectives. Prior work has largely tried to do this either symbolically or with black-box transformers. We present a middle ground between these two extremes: a compositional model reminiscent of neural module networks that can perform chained logical reasoning. This model first finds relevant sentences in the context and then chains them together using neural modules. Our model gives significant performance improvements (up to 29% relative error reduction when combined with a reranker) on ROPES, a recentlyintroduced complex reasoning dataset. | Multi-Step Inference for Reasoning Over Paragraphs |
d16514634 | Named entity recognition (NER) systems are often based on machine learning techniques to reduce the labor-intensive development of hand-crafted extraction rules and domain-dependent dictionaries. Nevertheless, time-consuming feature engineering is often needed to achieve state-of-the-art performance. In this study, we investigate the impact of such domain-specific features on the performance of recognizing and classifying mentions of pharmacological substances. We compare the performance of a system based on general features, which have been successfully applied to a wide range of NER tasks, with a system that additionally uses features generated from the output of an existing chemical NER tool and a collection of domain-specific resources. We demonstrate that acceptable results can be achieved with the former system. Still, our experiments show that using domain-specific features outperforms this general approach. Our system ranked first in the SemEval-2013 Task 9.1: Recognition and classification of pharmacological substances. | WBI-NER: The impact of domain-specific features on the performance of identifying and classifying mentions of drugs |
d227231477 | Research on document-level Neural Machine Translation (NMT) models has attracted increasing attention in recent years. Although the proposed works have proved that the inter-sentence information is helpful for improving the performance of the NMT models, what information should be regarded as context remains ambiguous. To solve this problem, we proposed a novel cache-based document-level NMT model which conducts dynamic caching guided by theme-rheme information. The experiments on NIST evaluation sets demonstrate that our proposed model achieves substantial improvements over the state-of-the-art baseline NMT models. As far as we know, we are the first to introduce theme-rheme theory into the field of machine translation. * Corresponding author. This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http: //creativecommons.org/licenses/by/4.0/. | A Document-Level Neural Machine Translation Model with Dynamic Caching Guided by Theme-Rheme Information |
d588327 | We propose a novel method to learn negation expressions in a specialized (medical) domain. In our corpus, negations are annotated as 'flat' text spans. This allows for some infelicities in the mark-up of the ground truth, making it less than perfectly aligned with the underlying syntactic structure. Nonetheless, the negations thus captured are correct in intent, and thus potentially valuable. We succeed in training a model for detecting the negated predicates corresponding to the annotated negations, by re-mapping the corpus to anchor its 'flat' annotation spans into the predicate argument structure. Our key idea-re-mapping the negation instance spans to more uniform syntactic nodes-makes it possible to re-frame the learning task as a simpler one, and to leverage an imperfect resource in a way which enables us to learn a high performance model. We achieve high accuracy for negation detection overall, 87%. Our re-mapping scheme can be constructively applied to existing flatly annotated resources for other tasks where syntactic context is vital. | Learning Structures of Negations from Flat Annotations |
d233210453 | Extracting semantic information on measurements and counts is an important topic in terms of analyzing scientific discourses. The 8th task of SemEval-2021: Counts and Measurements (MeasEval) aimed to boost research in this direction by providing a new dataset on which participants train their models to extract meaningful information on measurements from scientific texts. The competition is composed of five subtasks that build on top of each other: (1) quantity span identification, (2) unit extraction from the identified quantities and their value modifier classification, (3) span identification for measured entities and measured properties, (4) qualifier span identification, and (5) relation extraction between the identified quantities, measured entities, measured properties, and qualifiers. We approached these challenges by first identifying the quantities, extracting their units of measurement, classifying them with corresponding modifiers, and afterwards using them to jointly solve the last three subtasks in a multiturn question answering manner. Our best performing model obtained an overlapping F1score of 36.91% on the test set. | UPB at SemEval-2021 Task 8: Extracting Semantic Information on Measurements as Multi-Turn Question Answering |
d218470427 | Biomedical named entities often play important roles in many biomedical text mining tools. However, due to the incompleteness of provided synonyms and numerous variations in their surface forms, normalization of biomedical entities is very challenging. In this paper, we focus on learning representations of biomedical entities solely based on the synonyms of entities. To learn from the incomplete synonyms, we use a model-based candidate selection and maximize the marginal likelihood of the synonyms present in top candidates. Our model-based candidates are iteratively updated to contain more difficult negative samples as our model evolves. In this way, we avoid the explicit pre-selection of negative samples from more than 400K candidates. On four biomedical entity normalization datasets having three different entity types (disease, chemical, adverse reaction), our model BIOSYN consistently outperforms previous state-of-the-art models almost reaching the upper bound on each dataset. | Biomedical Entity Representations with Synonym Marginalization |
d10730965 | The goal of keyphrase extraction is to automatically identify the most salient phrases from documents. The technique has a wide range of applications such as rendering a quick glimpse of a document, or extracting key content for further use. While previous work often assumes keyphrases are a static property of a given documents, in many applications, the appropriate set of keyphrases that should be extracted depends on the set of documents that are being considered together. In particular, good keyphrases should not only accurately describe the content of a document, but also reveal what discriminates it from the other documents. In this paper, we study this problem of extracting discriminative keyphrases. In particularly, we propose to use the hierarchical semantic structure between candidate keyphrases to promote keyphrases that have the right level of specificity to clearly distinguish the target document from others. We show that such knowledge can be used to construct better discriminative keyphrase extraction systems that do not assume a static, fixed set of keyphrases for a document. We show how this helps identify key expertise of authors from their papers, as well as competencies covered by online courses within different domains. | Extracting Discriminative Keyphrases with Learned Semantic Hierarchies Cyril Goutte NRC Canada Multilingual Text Processing Ottawa ON, Canada |
d235313469 | Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i.e., pattern recognition, logical reasoning, coreference reasoning, etc.) related to the relation between | Discriminative Reasoning for Document-level Relation Extraction |
d235313508 | Temporal Knowledge Graphs (Temporal KGs) extend regular Knowledge Graphs by providing temporal scopes (e.g., start and end times) on each edge in the KG. While Question Answering over KG (KGQA) has received some attention from the research community, QA over Temporal KGs (Temporal KGQA) is a relatively unexplored area. Lack of broadcoverage datasets has been another factor limiting progress in this area. We address this challenge by presenting CRONQUESTIONS, the largest known Temporal KGQA dataset, clearly stratified into buckets of structural complexity. CRONQUESTIONS expands the only known previous dataset by a factor of 340×. We find that various state-of-the-art KGQA methods fall far short of the desired performance on this new dataset. In response, we also propose CRONKGQA, a transformerbased solution that exploits recent advances in Temporal KG embeddings, and achieves performance superior to all baselines, with an increase of 120% in accuracy over the next best performing method. Through extensive experiments, we give detailed insights into the workings of CRONKGQA, as well as situations where significant further improvements appear possible. In addition to the dataset, we have released our code as well. | Question Answering Over Temporal Knowledge Graphs |
d196173298 | Learning with minimal data is one of the key challenges in the development of practical, production-ready goal-oriented dialogue systems. In a real-world enterprise setting where dialogue systems are developed rapidly and are expected to work robustly for an evergrowing variety of domains, products, and scenarios, efficient learning from a limited number of examples becomes indispensable.In this paper, we introduce a technique to achieve state-of-the-art dialogue generation performance in a few-shot setup, without using any annotated data. We do this by leveraging background knowledge from a larger, more highly represented dialogue sourcenamely, the MetaLWOz dataset. We evaluate our model on the Stanford Multi-Domain Dialogue Dataset, consisting of human-human goal-oriented dialogues in in-car navigation, appointment scheduling, and weather information domains.We show that our few-shot approach achieves state-of-the art results on that dataset by consistently outperforming the previous best model in terms of BLEU and Entity F1 scores, while being more data-efficient by not requiring any data annotation. | Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach |
d196203256 | In this paper, we explore a new approach for automated chess commentary generation, which aims to generate chess commentary texts in different categories (e.g., description, comparison, planning, etc.). We introduce a neural chess engine into text generation models to help with encoding boards, predicting moves, and analyzing situations. By jointly training the neural chess engine and the generation models for different categories, the models become more effective. We conduct experiments on 5 categories in a benchmark Chess Commentary dataset and achieve inspiring results in both automatic and human evaluations. | |
d46925114 | This paper presents two novel datasets and a random-forest classifier to automatically predict literal vs. non-literal language usage for a highly frequent type of multi-word expression in a low-resource language, i.e., Estonian. We demonstrate the value of language-specific indicators induced from theoretical linguistic research, which outperform a high majority baseline when combined with languageindependent features of non-literal language (such as abstractness). | Combining Abstractness and Language-specific Theoretical Indicators for Detecting Non-Literal Usage of Estonian Particle Verbs |
d222141642 | Many tasks in natural language processing involve predicting structured outputs, e.g., sequence labeling, semantic role labeling, parsing, and machine translation. Researchers are increasingly applying deep representation learning to these problems, but the structured component of these approaches is usually quite simplistic. In this work, we propose several high-order energy terms to capture complex dependencies among labels in sequence labeling, including several that consider the entire label sequence. We use neural parameterizations for these energy terms, drawing from convolutional, recurrent, and selfattention networks. We use the framework of learning energy-based inference networks(Tu and Gimpel, 2018)for dealing with the difficulties of training and inference with such models. We empirically demonstrate that this approach achieves substantial improvement using a variety of high-order energy terms on four sequence labeling tasks, while having the same decoding speed as simple, local classifiers. We also find high-order energies to help in noisy data conditions. | An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks |
d222141709 | We propose a new task in the area of computational creativity: acrostic poem generation in English. Acrostic poems are poems that contain a hidden message; typically, the first letter of each line spells out a word or short phrase. We define the task as a generation task with multiple constraints: given an input word, 1) the initial letters of each line should spell out the provided word, 2) the poem's semantics should also relate to it, and 3) the poem should conform to a rhyming scheme. We further provide a baseline model for the task, which consists of a conditional neural language model in combination with a neural rhyming model. Since no dedicated datasets for acrostic poem generation exist, we create training data for our task by first training a separate topic prediction model on a small set of topic-annotated poems and then predicting topics for additional poems. Our experiments show that the acrostic poems generated by our baseline are received well by humans and do not lose much quality due to the additional constraints. Last, we confirm that poems generated by our model are indeed closely related to the provided prompts, and that pretraining on Wikipedia can boost performance. | Acrostic Poem Generation |
d225039774 | While recent advances in language modeling has resulted in powerful generation models, their generation style remains implicitly dependent on the training data and can not emulate a specific target style. Leveraging the generative capabilities of a transformer-based language models, we present an approach to induce certain target-author attributes by incorporating continuous multi-dimensional lexical preferences of an author into generative language models. We introduce rewarding strategies in a reinforcement learning framework that encourages the use of words across multiple categorical dimensions, to varying extents. Our experiments demonstrate that the proposed approach can generate text that distinctively aligns with a given target author's lexical style. We conduct quantitative and qualitative comparisons with competitive and relevant baselines to illustrate the benefits of the proposed approach. | Incorporating Stylistic Lexical Preferences in Generative Language Models |
d52011355 | In view of the differences between the annotations of micro and macro discourse relationships, this paper describes the relevant experiments on the construction of the Macro Chinese Discourse Treebank (MCDTB), a higher-level Chinese discourse corpus. Following RST (Rhetorical Structure Theory), we annotate the macro discourse information, including discourse structure, nuclearity and relationship, and the additional discourse information, including topic sentences, lead and abstract, to make the macro discourse annotation more objective and accurate. Finally, we annotated 720 articles with a Kappa value greater than 0.6. Preliminary experiments on this corpus verify the computability of MCDTB. | MCDTB: A Macro-Level Chinese Discourse TreeBank |
d27641057 | State-of-the-art methods for proteinprotein interaction (PPI) extraction are primarily feature-based or kernel-based by leveraging lexical and syntactic information. But how to incorporate such knowledge in the recent deep learning methods remains an open question. In this paper, we propose a multichannel dependency-based convolutional neural network model (McDepCNN). It applies one channel to the embedding vector of each word in the sentence, and another channel to the embedding vector of the head of the corresponding word. Therefore, the model can use richer information obtained from different channels. Experiments on two public benchmarking datasets, AIMed and BioInfer, demonstrate that McDepCNN compares favorably to the state-of-the-art rich-feature and single-kernel based methods. In addition, McDepCNN achieves 24.4% relative improvement in F1-score over the state-of-the-art methods on crosscorpus evaluation and 12% improvement in F1-score over kernel-based methods on "difficult" instances. These results suggest that McDepCNN generalizes more easily over different corpora, and is capable of capturing long distance features in the sentences. | Deep learning for extracting protein-protein interactions from biomedical literature |
d2036954 | The development of natural language interfaces (NLI's) for databases has been a challenging problem in natural language processing (NLP) since the 1970's. The need for NLI's has become more pronounced due to the widespread access to complex databases now available through the Internet. A challenging problem for empirical NLP is the automated acquisition of NLI's from training examples. We present a method for integrating statistical and relational learning techniques for this task which exploits the strength of both approaches. Experimental results from three different domains suggest that such an approach is more robust than a previous purely logicbased approach. | Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing |
d222142503 | Graph embedding (GE) methods embed nodes (and/or edges) in graph into a low-dimensional semantic space, and have shown its effectiveness in modeling multi-relational data. However, existing GE models are not practical in real-world applications since it overlooked the streaming nature of incoming data. To address this issue, we study the problem of continual graph representation learning which aims to continually train a GE model on new data to learn incessantly emerging multirelational data while avoiding catastrophically forgetting old learned knowledge. Moreover, we propose a disentangle-based continual graph representation learning (DiCGRL) framework inspired by the human's ability to learn procedural knowledge.The experimental results show that DiCGRL could effectively alleviate the catastrophic forgetting problem and outperform state-of-the-art continual learning models. The code and datasets are released on https://github. | Disentangle-based Continual Graph Representation Learning |
d237581087 | Every day, millions of people sacrifice their privacy and browsing habits in exchange for online machine translation. Companies and governments with confidentiality requirements often ban online translation or pay a premium to disable logging. To bring control back to the end user and demonstrate speed, we developed translateLocally. Running locally on a desktop or laptop CPU, translateLocally delivers cloud-like translation speed and quality even on 10 year old hardware. The open-source software is based on Marian and runs on Linux, Windows, and macOS. | TranslateLocally: Blazing-fast translation running on the local CPU |
d235294032 | Personalized news recommendation methods are widely used in online news services. These methods usually recommend news based on the matching between news content and user interest inferred from historical behaviors. However, these methods usually have difficulties in making accurate recommendations to cold-start users, and tend to recommend similar news with those users have read. In general, popular news usually contain important information and can attract users with different interests. Besides, they are usually diverse in content and topic. Thus, in this paper we propose to incorporate news popularity information to alleviate the cold-start and diversity problems for personalized news recommendation. In our method, the ranking score for recommending a candidate news to a target user is the combination of a personalized matching score and a news popularity score. The former is used to capture the personalized user interest in news. The latter is used to measure timeaware popularity of candidate news, which is predicted based on news content, recency, and real-time CTR using a unified framework. Besides, we propose a popularity-aware user encoder to eliminate the popularity bias in user behaviors for accurate interest modeling. Experiments on two real-world datasets show our method can effectively improve the accuracy and diversity for news recommendation. | PP-Rec: News Recommendation with Personalized User Interest and Time-aware News Popularity |
d222103842 | Information Extraction (IE) for semistructured document images is often approached as a sequence tagging problem by classifying each recognized input token into one of the IOB (Inside, Outside, and Beginning) categories. However, such problem setup has two inherent limitations that (1) it cannot easily handle complex spatial relationships and(2)it is not suitable for highly structured information, which are nevertheless frequently observed in real-world document images. To tackle these issues, we first formulate the IE task as spatial dependency parsing problem that focuses on the relationship among text tokens in the documents. Under this setup, we then propose SPADE (SPAtial DEpendency parser) that models highly complex spatial relationships and an arbitrary number of information layers in the documents in an end-to-end manner. We evaluate it on various kinds of documents such as receipts, name cards, forms, and invoices, and show that it achieves a similar or better performance compared to strong baselines including BERT-based IOB taggger. | Spatial Dependency Parsing for Semi-Structured Document Information Extraction |
d221836061 | Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. Model performance on KPG tasks has increased significantly with evolving deep learning research. However, there lacks a comprehensive comparison among different model designs, and a thorough investigation on related factors that may affect a KPG system's generalization performance. In this empirical study, we aim to fill this gap by providing extensive experimental results and analyzing the most crucial factors impacting the generalizability of KPG models. We hope this study can help clarify some of the uncertainties surrounding the KPG task and facilitate future research on this topic. | An Empirical Study on Neural Keyphrase Generation |
d27285969 | I demonstrate here an experiment of word sense disambiguation method based on the Self-Organizing Map (SOM) and a pre-existing set of tools for analyzing text in Finnish. It is given a Semantic Web ontology as a reference model, and a related Finnish text corpus with sample term tagging related to the ontology concepts. The experiment is based on "OntoR", a previous experiment on SOM-based ontology term tagging for English. In this work the OntoR model is adapted to the Finnish language, and it is trained on a small text example with hand-picked concept annotations. This computational model can be considered useful for Information Retrieval and concept harvesting purposes in a specific domain where a limited training data set is available. The model adapted to Finnish text analysis stands on OMORFI and HFST morphological analysis, and uses the SOM-PAK library for unsupervised clustering, and ontology concept tagging and further for concept harvesting in Semantic Web ontology development.TiivistelmäKehitän luonnollisessa kielessä ilmenevien sanojen merkitysten erotteluun sopivaa automaattista koneoppivaa työkalua. Laskennallinen malli perustuu itseoppivaan karttaan (SOM, Self-Organizing Map) ja annettuun suomenkieliseen semanttisen webin ontologiaan. Malli oppii tunnistamaan käsitteiden ilmenemistä mallitekstistä, johon on annotoitu (tagattu) malliksi aiemmin laaditun ongologian käsitteitä. Koe liittyy aiemmin englanninkielisten käsitteiden taggaamiseen liittyvään OntoR-koejärjestelyyn joka tutki tekstisyötteessä ilmenevien termien liittämistä SOM-kartan soluihin malliksi annetun annotoidun tekstiesimerkin avulla. Tällainen malli oppii annetun käsitemallin huomattavan niukalla esimerkkiaineistolla ja sopii käyttökohteisiin joissa ei ole tarjolla riittävän suurta datamäärää syvän oppimisen neuroverkkomallin opettamiseksi. Suomenkielisen kokeen morfologisen analyysin pohjalla on OMORFI-ja HFST-työkalut. Koneoppimisen toteuttava SOM-kartta lasketaan SOM-PAK-ohjelmistopaketin avulla. Kehitettyä laskennallista mallia käytetään käsitteiden tunnistamisen lisäksi myös uusien ontologiakäsitteiden ehdokkaiden löytämiseksi. | Building a Finnish SOM-based ontology concept tagger and harvester |
d245906090 | We describe NorDiaChange: the first diachronic semantic change dataset for Norwegian. NorDiaChange comprises two novel subsets, covering about 80 Norwegian nouns manually annotated with graded semantic change over time. Both datasets follow the same annotation procedure and can be used interchangeably as train and test splits for each other. NorDiaChange covers the time periods related to pre-and post-war events, oil and gas discovery in Norway, and technological developments. The annotation was done using the DURel framework and two large historical Norwegian corpora. NorDiaChange is published in full under a permissive licence, complete with raw annotation data and inferred diachronic word usage graphs (DWUGs). | NorDiaChange: Diachronic Semantic Change Dataset for Norwegian |
d17033179 | We report the results of our experiments in the context of the NEWS 2015 Shared Task on Transliteration. We focus on methods of combining multiple base systems, and leveraging transliterations from multiple languages. We show error reductions over the best base system of up to 10% when using supplemental transliterations, and up to 20% when using system combination. We also discuss the quality of the shared task datasets. | Multiple System Combination for Transliteration |
d2486328 | This paper describes the systems submitted by the University of San Francisco (USF) to Semeval-2014 Task 4, Aspect Based Sentiment Analysis (ABSA), which provides labeled data in two domains, laptops and restaurants. For the constrained condition of both the aspect term extraction and aspect term polarity tasks, we take a supervised machine learning approach using a combination of lexical, syntactic, and baseline sentiment features. Our extraction approach is inspired by a chunking approach, based on its strong past results on related tasks. Our system performed slightly below average compared to other submissions, possibly because we use a simpler classification model than prior work. Our polarity labeling approach uses two baseline hand-built sentiment classifiers as features in addition to lexical and syntactic features, and performed in the top ten of other constrained systems on both domains. | USF: Chunking for Aspect Term Identification & Polarity Classification |
d52130958 | We reassess a recent study(Hassan et al., 2018)that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pairwise ranking and considering three variables that were not taken into account in that previous study: the language in which the source side of the test set was originally written, the translation proficiency of the evaluators, and the provision of inter-sentential context. If we consider only original source text (i.e. not translated from another language, or translationese), then we find evidence showing that human parity has not been achieved. We compare the judgments of professional translators against those of non-experts and discover that those of the experts result in higher inter-annotator agreement and better discrimination between human and machine translations. In addition, we analyse the human translations of the test set and identify important translation issues. Finally, based on these findings, we provide a set of recommendations for future human evaluations of MT. | Attaining the Unattainable? Reassessing Claims of Human Parity in Neural Machine Translation |
d216869396 | How does language model pretraining help transfer learning? We consider a simple ablation technique for determining the impact of each pretrained layer on transfer task performance. This method, partial reinitialization, involves replacing different layers of a pretrained model with random weights, then finetuning the entire model on the transfer task and observing the change in performance. This technique reveals that in BERT, layers with high probing performance on downstream GLUE tasks are neither necessary nor sufficient for high accuracy on those tasks. Furthermore, the benefit of using pretrained parameters for a layer varies dramatically with finetuning dataset size: parameters that provide tremendous performance improvement when data is plentiful may provide negligible benefits in data-scarce settings. These results reveal the complexity of the transfer learning process, highlighting the limitations of methods that operate on frozen models or single data samples. | Investigating Transferability in Pretrained Language Models |
d216552978 | As a crucial step in extractive document summarization, learning cross-sentence relations has been explored by a plethora of approaches. An intuitive way is to put them in the graphbased neural network, which has a more complex structure for capturing inter-sentence relationships. In this paper, we present a heterogeneous graph-based neural network for extractive summarization (HETERSUMGRAPH), which contains semantic nodes of different granularity levels apart from sentences. These additional nodes act as the intermediary between sentences and enrich the cross-sentence relations. | Heterogeneous Graph Neural Networks for Extractive Document Summarization |
d222177203 | Sequence model based NLP applications can be large. Yet, many applications that benefit from them run on small devices with very limited compute and storage capabilities, while still having run-time constraints. As a result, there is a need for a compression technique that can achieve significant compression without negatively impacting inference run-time and task accuracy. This paper proposes a new compression technique called Hybrid Matrix Factorization that achieves this dual objective. HLF improves low-rank matrix factorization (LMF) techniques by doubling the rank of the matrix using an intelligent hybrid-structure leading to better accuracy than LMF. Further, by preserving dense matrices, it leads to faster inference run-time than pruning or structure matrix based compression technique. We evaluate the impact of this technique on 5 NLP benchmarks across multiple tasks (Translation, Intent Detection, Language Modeling) and show that for similar accuracy values and compression factors, HLF can achieve more than 2.32× faster inference run-time than pruning and 16.77% better accuracy than LMF. | Rank and run-time aware compression of NLP Applications |
d2689604 | In this paper, we show that generative classifiers are capable of learning non-linear decision boundaries and that non-linear generative models can outperform a number of linear classifiers on some text categorization tasks.We first prove that 3-layer multinomial hierarchical generative (Bayesian) classifiers, under a particular independence assumption, can only learn the same linear decision boundaries as a multinomial naive Bayes classifier.We then go on to show that making a different independence assumption results in nonlinearization, thereby enabling us to learn non-linear decision boundaries.We finally evaluate the performance of these non-linear classifiers on a series of text classification tasks. | Learning Non-Linear Functions for Text Classification |
d15701406 | Compositional Distributional Semantics Models (CDSMs) are traditionally seen as an entire different world with respect to Tree Kernels (TKs). In this paper, we show that under a suitable regime these two approaches can be regarded as the same and, thus, structural information and distributional semantics can successfully cooperate in CSDMs for NLP tasks. Leveraging on distributed trees, we present a novel class of CDSMs that encode both structure and distributional meaning: the distributed smoothed trees (DSTs). By using DSTs to compute the similarity among sentences, we implicitly define the distributed smoothed tree kernels (DSTKs). Experiment with our DSTs show that DSTKs approximate the corresponding smoothed tree kernels (STKs). Thus, DSTs encode both structural and distributional semantics of text fragments as STKs do. Experiments on RTE and STS show that distributional semantics encoded in DSTKs increase performance over structure-only kernels. | Towards Syntax-aware Compositional Distributional Semantic Models |
d8170227 | We address the problem of selecting nondomain-specific language model training data to build auxiliary language models for use in tasks such as machine translation. Our approach is based on comparing the cross-entropy, according to domainspecific and non-domain-specifc language models, for each sentence of the text source used to produce the latter language model. We show that this produces better language models, trained on less data, than both random data selection and two other previously proposed methods. | Intelligent Selection of Language Model Training Data |
d226222211 | Prior research notes that BERT's computational cost grows quadratically with sequence length thus leading to longer training times, higher GPU memory constraints and carbon emissions. While recent work seeks to address these scalability issues at pre-training, these issues are also prominent in fine-tuning especially for long sequence tasks like document classification. Our work thus focuses on optimizing the computational cost of fine-tuning for document classification. We achieve this by complementary learning of both topic and language models in a unified framework, named TopicBERT. This significantly reduces the number of self-attention operations -a main performance bottleneck. Consequently, our model achieves a 1.4x ( 40%) speedup with 40% reduction in CO 2 emission while retaining 99.9% performance over 5 datasets. | TopicBERT for Energy Efficient Document Classification |
d233210761 | We analyze if large language models are able to predict patterns of human reading behavior. We compare the performance of language-specific and multilingual pretrained transformer models to predict reading time measures reflecting natural human sentence processing on Dutch, English, German, and Russian texts. This results in accurate models of human reading behavior, which indicates that transformer models implicitly encode relative importance in language in a way that is comparable to human processing mechanisms. We find that BERT and XLM models successfully predict a range of eye tracking features. In a series of experiments, we analyze the cross-domain and cross-language abilities of these models and show how they reflect human sentence processing. | Multilingual Language Models Predict Human Reading Behavior |
d202237637 | Grounding a pronoun to a visual object it refers to requires complex reasoning from various information sources, especially in conversational scenarios. For example, when people in a conversation talk about something all speakers can see, they often directly use pronouns (e.g., it) to refer to it without previous introduction. This fact brings a huge challenge for modern natural language understanding systems, particularly conventional contextbased pronoun coreference models. To tackle this challenge, in this paper, we formally define the task of visual-aware pronoun coreference resolution (PCR) and introduce VisPro, a large-scale dialogue PCR dataset, to investigate whether and how the visual information can help resolve pronouns in dialogues. We then propose a novel visual-aware PCR model, VisCoref, for this task and conduct comprehensive experiments and case studies on our dataset. Results demonstrate the importance of the visual information in this PCR case and show the effectiveness of the proposed model. | What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues |
d216641617 | Parody is a figurative device used to imitate an entity for comedic or critical purposes and represents a widespread phenomenon in social media through many popular parody accounts. In this paper, we present the first computational study of parody. We introduce a new publicly available data set of tweets from real politicians and their corresponding parody accounts. We run a battery of supervised machine learning models for automatically detecting parody tweets with an emphasis on robustness by testing on tweets from accounts unseen in training, across different genders and across countries. Our results show that political parody tweets can be predicted with an accuracy up to 90%. Finally, we identify the markers of parody through a linguistic analysis. Beyond research in linguistics and political communication, accurately and automatically detecting parody is important to improving fact checking for journalists and analytics such as sentiment analysis through filtering out parodical utterances. | Analyzing Political Parody in Social Media |
d216036398 | Recently, pre-trained language models mostly follow the pre-training-then-finetuning paradigm and have achieved great performances on various downstream tasks. However, due to the aimlessness of pretraining and the small in-domain supervised data scale of fine-tuning, the two-stage models typically cannot capture the domain-specific and task-specific language patterns well. In this paper, we propose a selective masking task-guided pre-training method and add it between the general pre-training and fine-tuning. In this stage, we train the masked language modeling task on in-domain unsupervised data, which enables our model to effectively learn the domain-specific language patterns. To efficiently learn the task-specific language patterns, we adopt a selective masking strategy instead of the conventional random masking, which means we only mask the tokens that are important to the downstream task. Specifically, we define the importance of tokens as their impacts on the final classification results and use a neural model to learn the implicit selecting rules. Experimental results on two sentiment analysis tasks show that our method can achieve comparable or even better performance with less than 50% overall computation cost, which indicates our method is both effective and efficient. The source code will be released in the future. | Train No Evil: Selective Masking for Task-guided Pre-training |
d13495961 | The existing machine translation systems, whether phrase-based or neural, have relied almost exclusively on word-level modelling with explicit segmentation. In this paper, we ask a fundamental question: can neural machine translation generate a character sequence without any explicit segmentation? To answer this question, we evaluate an attention-based encoderdecoder with a subword-level encoder and a character-level decoder on four language pairs-En-Cs, En-De, En-Ru and En-Fiusing the parallel corpora from WMT'15. Our experiments show that the models with a character-level decoder outperform the ones with a subword-level decoder on all of the four language pairs. Furthermore, the ensembles of neural models with a character-level decoder outperform the state-of-the-art non-neural machine translation systems on En-Cs, En-De and En-Fi and perform comparably on En-Ru. | A Character-Level Decoder without Explicit Segmentation for Neural Machine Translation |
d218502386 | We address the problem of extractive question answering using document-level distant supervision, pairing questions and relevant documents with answer strings. We compare previously used probability space and distant supervision assumptions (assumptions on the correspondence between the weak answer string labels and possible answer mention spans). We show that these assumptions interact, and that different configurations provide complementary benefits. We demonstrate that a multiobjective model can efficiently combine the advantages of multiple assumptions and outperform the best individual formulation. Our approach outperforms previous state-of-the-art models by 4.3 points in F1 on TriviaQA-Wiki and 1.7 points in Rouge-L on NarrativeQA summaries. 1 | Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering |
d221970493 | Recent years the task of incomplete utterance rewriting has raised a large attention. Previous works usually shape it as a machine translation task and employ sequence to sequence based architecture with copy mechanism. In this paper, we present a novel and extensive approach, which formulates it as a semantic segmentation task. Instead of generating from scratch, such a formulation introduces edit operations and shapes the problem as prediction of a word-level edit matrix. Benefiting from being able to capture both local and global information, our approach achieves state-ofthe-art performance on several public datasets. Furthermore, our approach is four times faster than the standard approach in inference. | Incomplete Utterance Rewriting as Semantic Segmentation |
d8884032 | In order to be able to systematically link compounds in GermaNet to their constituent parts, compound splitting needs to be applied recursively and has to identify the immediate constituents at each level of analysis. Existing tools for compound splitting for German only offer an analysis of all component parts of a compound at once without any grouping of subconstituents. Thus, existing tools for splitting compounds were adapted to overcome this issue. Algorithms combining three heterogeneous kinds of compound splitters are developed to achieve better results. The best overall result with an accuracy of 92.42% is achieved by a hybrid combined compound splitter that takes into account all knowledge provided by the individual compound splitters, and in addition some domain knowledge about German derivation morphology and compounding. | Determining Immediate Constituents of Compounds in GermaNet |
d6275358 | Sentiment analysis is one of the recent, highly dynamic fields in Natural Language Processing. Most existing approaches are based on word-level analysis of texts and are able to detect only explicit expressions of sentiment. In this paper, we present an approach towards automatically detecting emotions (as underlying components of sentiment) from contexts in which no clues of sentiment appear, based on commonsense knowledge. The resource we built towards this aim -EmotiNet -is a knowledge base of concepts with associated affective value. Preliminary evaluations show that this approach is appropriate for the task of implicit emotion detection, thus improving the performance of sentiment detection and classification in text. | Detecting Implicit Expressions of Sentiment in Text Based on Commonsense Knowledge |
d5370063 | Words unknown to the lexicon present a substantial problem to NLP modules that rely on morphosyntactic information, such as part-of-speech taggers or syntactic parsers. In this paper we present a technique for fully automatic acquisition of rules that guess possible part-of-speech tags for unknown words using their starting and ending segments. The learning is performed from a general-purpose lexicon and word frequencies collected from a raw corpus. Three complimentary sets of word-guessing rules are statistically induced: prefix morphological rules, suffix morphological rules and ending-guessing rules. Using the proposed technique, unknown-word-guessing rule sets were induced and integrated into a stochastic tagger and a rule-based tagger, which were then applied to texts with unknown words. | Automatic Rule Induction for Unknown-Word Guessing |
d222125120 | The meaning of a word is closely linked to sociocultural factors that can change over time and location, resulting in corresponding meaning changes. Taking a global view of words and their meanings in a widely used language, such as English, may require us to capture more refined semantics for use in time-specific or location-aware situations, such as the study of cultural trends or language use. However, popular vector representations for words do not adequately include temporal or spatial information. In this work, we present a model for learning word representation conditioned on time and location. In addition to capturing meaning changes over time and location, we require that the resulting word embeddings retain salient semantic and geometric properties. We train our model on time-and locationstamped corpora, and show using both quantitative and qualitative evaluations that it can capture semantics across time and locations. We note that our model compares favorably with the state-of-the-art for time-specific embedding, and serves as a new benchmark for location-specific embeddings. | Enriching Word Embeddings with Temporal and Spatial Information |
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