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d15048566 | We report ongoing work on the development of agents that can implicitly coordinate with their partners in referential tasks, taking as a case study colour terms. We describe algorithms for generation and resolution of colour descriptions and report results of experiments on how humans use colour terms for reference in production and comprehension. | Towards a Flexible Semantics: Colour Terms in Collaborative Reference Tasks |
d18115003 | In this paper, we present the acquisition and labeling processes of the EDECAN-SPORTS corpus, which is a corpus that is oriented to the development of multimodal dialog systems acquired in Spanish and Catalan. Two Wizards of Oz were used in order to better simulate the behavior of an actual system in terms of both the information used by the different modules and the communication mechanisms between these modules. User and system dialog-act labeling, as well as other information, have been obtained automatically using this acquisition method Some preliminary experimental results with the acquired corpus show the appropriateness of the proposed acquisition method for the development of dialog systems. | The acquisition and dialog-act labeling of the EDECAN-SPORTS corpus |
d243865119 | We describe two approaches to single-root dependency parsing that yield significant speed ups in such parsing. One approach has been previously used in dependency parsers in practice, but remains undocumented in the parsing literature, and is considered a heuristic. We show that this approach actually finds the optimal dependency tree. The second approach relies on simple reweighting of the inference graph being input to the dependency parser and has an optimal running time. Here, we again show that this approach is fully correct and identifies the highest-scoring parse tree. Our experiments demonstrate a manyfold speed up compared to a previous graph-based state-of-the-art parser without any loss in accuracy or optimality. 1 | A Root of a Problem: Optimizing Single-Root Dependency Parsing |
d1159978 | In this paper we present a Lexicalized Feature-based Tree-Adjoining Grammar analysis for a type of nominal predicate that occurs in combination with the light verbs "do" and "be" (Hindi kar and ho respectively). Light verb constructions are a challenge for computational grammars because they are a highly productive predicational strategy in Hindi. Such nominals have been discussed in the literature(Mohanan, 1997;Ahmed and Butt, 2011;Bhatt et al., 2013), but this work is a first attempt at a Tree-Adjoining Grammar (TAG) representation. We look at three possibilities for the design of elementary trees in TAG and explore one option in depth using Hindi data. In this analysis, the nominal is represented with all the arguments of the light verb construction, while the light verb adjoins into its elementary tree. | Light verb constructions with 'do' and 'be' in Hindi: A TAG analysis |
d11868924 | Information extraction systems automatically extract structured information from machine-readable documents, such as newswire, web, and multimedia. Despite significant improvement, the performance is far from perfect. Hence, it is useful to accurately estimate confidence in the correctness of the extracted information. Using the Knowledge Base Population Slot Filling task as a case study, we propose a confidence estimation model based on the Maximum Entropy framework, obtaining an average precision of 83.5%, Pearson coefficient of 54.2%, and 2.3% absolute improvement in F-measure score through a weighted voting strategy. | Confidence Estimation for Knowledge Base Population |
d6416117 | The usual concern when opting for a rule-based or a hybrid machine translation (MT) system is how much effort is required to adapt the system to a different language pair or a new domain. In this paper, we describe a way of adapting an existing hybrid MT system to a new language pair, and show that such a system can outperform a standard phrase-based statistical machine translation system with an average of 10 persons/month of work. This is specifically important in the case of domain-specific MT for which there is not enough parallel data for training a statistical machine translation system. | Bootstrapping a Hybrid MT System to a New Language Pair |
d237099285 | Many existing chatbots do not effectively support mixed initiative, forcing their users to either respond passively or lead constantly. We seek to improve this experience by introducing new mechanisms to encourage user initiative in social chatbot conversations. Since user initiative in this setting is distinct from initiative in human-human or task-oriented dialogue, we first propose a new definition that accounts for the unique behaviors users take in this context. Drawing from linguistics, we propose three mechanisms to promote user initiative: back-channeling, personal disclosure, and replacing questions with statements. We show that simple automatic metrics of utterance length, number of noun phrases, and diversity of user responses correlate with human judgement of initiative. Finally, we use these metrics to suggest that these strategies do result in statistically significant increases in user initiative, where frequent, but not excessive, back-channeling is the most effective strategy. | Effective Social Chatbot Strategies for Increasing User Initiative |
d6615977 | We describe the methods and resources used to build FinnTreeBank-3, a 76.4 million token corpus of Finnish with automatically produced morphological and dependency syntax analyses. Starting from a definition of the target dependency scheme, we show how existing resources are transformed to conform to this definition and subsequently used to develop a parsing pipeline capable of processing a large-scale corpus. An independent formal evaluation demonstrates high accuracy of both morphological and syntactic annotation layers. The parsed corpus is freely available within the FIN-CLARIN infrastructure project. | Building a Large Automatically Parsed Corpus of Finnish |
d258765248 | Automatic text simplification (ATS) describes the automatic transformation of a text from a complex form to a less complex form. Many modern ATS techniques need large parallel corpora of standard and simplified text, but such data does not exist for many languages. One way to overcome this issue is to create pseudo-parallel corpora by dividing existing corpora into standard and simple parts. In this work, we explore the creation of Swedish pseudoparallel monolingual corpora by the application of different feature representation methods, sentence alignment algorithms, and indexing approaches, on a large monolingual corpus. The different corpora are used to fine-tune a sentence simplification system based on BART, which is evaluated with standard evaluation metrics for automatic text simplification. | Constructing Pseudo-parallel Swedish Sentence Corpora for Automatic Text Simplification |
d8221706 | 1Comparisons as coherence relations This paper considers how comparison relations can be integrated within an RST-like model of discourse. We will consider two relations, SIMILARITY (prototypically signalled by connectives like also and too) and CONTRAST (prototypically signalled by connectives like whereas and while). On the face of it. such relations should be easy to fit within an account like RST's. However, they each exhibit certain idiosyncracies. For instance, the CONTRAST relation in RST is unusual in having a multinuclear structure, rather than the typical nucleus-satellite structure (Mann and Thompson, 1988). SIMILARITY is unusual in that its associated connectives can apparently violate structural constraints of RST, linking the span in which they appear to structurally inaccessible spans, as in the following text:(1) I have two brothers. John is a student: he majors in history. He likes water polo, and he plays the drums. Bill is at high school. His main interest is drama. He also studies history, but he doesn't like it much.Our suggestion for integrating comparison relations within a model of discourse relations derives from a method of defining relations in terms of presupposed defensible rules. This method will be outlined for causal/inferential relations, and then adapted to comparative relations. | Similarity and contrast relations and inductive rules |
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d207852737 | Fake news has altered society in negative ways in politics and culture. It has adversely affected both online social network systems as well as offline communities and conversations. Using automatic machine learning classification models is an efficient way to combat the widespread dissemination of fake news. However, a lack of effective, comprehensive datasets has been a problem for fake news research and detection model development. Prior fake news datasets do not provide multimodal text and image data, metadata, comment data, and fine-grained fake news categorization at the scale and breadth of our dataset. We present Fakeddit, a novel multimodal dataset consisting of over 1 million samples from multiple categories of fake news. After being processed through several stages of review, the samples are labeled according to 2-way, 3-way, and 6-way classification categories through distant supervision. We construct hybrid text+image models and perform extensive experiments for multiple variations of classification, demonstrating the importance of the novel aspect of multimodality and fine-grained classification unique to Fakeddit. | r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection |
d1682678 | In this paper, we discuss the application of UBIU to the CONLL-2011 shared task on "Modeling Unrestricted Coreference" in OntoNotes. The shared task concentrates on the detection of coreference not only in noun phrases but also involving verbs. The information provided for the closed track included WordNet as well as corpus generated number and gender information. Our system shows no improvement when using WordNet information, and the number information proved less reliable than the information in the part of speech tags. | UBIU: A Robust System for Resolving Unrestricted Coreference |
d11708774 | We introduce an incremental model for coreference resolution that competed in the CoNLL 2011 shared task (open regular). We decided to participate with our baseline model, since it worked well with two other datasets. The benefits of an incremental over a mention-pair architecture are: a drastic reduction of the number of candidate pairs, a means to overcome the problem of underspecified items in pairwise classification and the natural integration of global constraints such as transitivity. We do not apply machine learning, instead the system uses an empirically derived salience measure based on the dependency labels of the true mentions. Our experiments seem to indicate that such a system already is on par with machine learning approaches. | An Incremental Model for Coreference Resolution with Restrictive Antecedent Accessibility |
d7340788 | Understanding lexical characteristics of clinical documents is the foundation of sublanguage based Medical Language Processing (MLP) approach. However, there are limited studies focused on the lexical characters of Chinese clinical documents. In this study, a lexical characteristics analysis on both syntactic and semantic levels was conducted in a clinical corpus which contains 3,500 clinical documents generated during daily practices. The analysis was based on the automatic tagging results of a lexiconbased part-of-speech (POS) and semantic tagging method. The medical lexicon contains 237,291 entries annotated with both semantic and syntactic classes. The normalized frequency of different terms, syntactic and semantic classes was calculated and visualized.Major contribution of this paper is providing a wide-coverage Chinese medical semantic lexicon and presenting the lexical characteristics of Chinese clinical documents. Both of these will lay a good foundation for sublanguage based MLP studies in China. | Lexical Characteristics Analysis of Chinese Clinical Documents |
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d236477887 | As part of scientific articles, grant information refers to funder names and their corresponding grant numbers. Extracting such funding information from articles is of significant importance to both academic and funding bodies. The studies on this topic face two major challenges: 1) no high-quality benchmark datasets; and 2) difficulties in extracting complex relationships between funders and grantIDs. In this paper, we present a novel pipeline framework called GrantRel, which consists of a funding sentence classifier, as well as a joint entity and relation extractor. For this purpose, we manually label two highquality datasets called Grant-SP and Grant-RE, respectively. In addition, our relation extraction (RE) model uses both position embedding and context embedding in an adaptivelearning way. The experiment results have demonstrated that our model outperforms several state-of-the-art BERT-based RE baselines as higher as 6.5% of F1 scores against the PubMed Central (PMC) test set and 3.5% of that against the arXiv test set. GrantRel-pos GrantRel-ctx GrantRel 7 17 21 26 7 9 12 | GrantRel: Grant Information Extraction via Joint Entity and Relation Extraction |
d42083832 | Word sense is ambiguous in natural language processing (NLP). This phenomenon is particularly keen in cases involving noun-verb (NV) w ord-pairs. This paper describes a sense-based noun-verb event frame (NVEF) identifier that can be used to disambiguate word sense in Chinese sentences effectively. A knowledge representation system (the NVEF-KR tree) for the NVEF sense-pair identifier is also proposed. We use the word sense of Hownet, which is a Chinese-English bilingual knowledge-base dictionary.Our experiment showed that the NVEF identifier was able to achieve 74.8% accuracy for the test sentences studied based only on NVEF sense-pair knowledge. By applying the techniques of longest syllabic NVEF-word-pair first and exclusion word checking, the sense accuracy for the same test sentences could be further improved to 93.7%. There were four major reasons for the incorrect cases: (1) lack of a bottom-up tagger, (2) lack of non-NVEF knowledge, (3) inadequate word segmentation, and (4) lack of a multi-NVEF analyzer. If these four problems could be resolved, the accuracy would reach 98.9%.The results of this study indicate that NVEF sense-pair knowledge is effective for word sense disambiguation and is likely to be important for general NLP. | Word Sense Disambiguation and Sense-Based NV Event Frame Identifier |
d8966691 | We investigate the task of medical concept coreference resolution in clinical text using two semi-supervised methods, co-training and multi-view learning with posterior regularization. By extracting semantic and temporal features of medical concepts found in clinical text, we create conditionally independent data views; co-training MaxEnt classifiers on this data works almost as well as supervised learning for the task of pairwise coreference resolution of medical concepts. We also train Max-Ent models with expectation constraints, using posterior regularization, and find that posterior regularization performs comparably to or slightly better than co-training. We describe the process of semantic and temporal feature extraction and demonstrate our methods on a corpus of case reports from the New England Journal of Medicine and a corpus of patient narratives obtained from The Ohio State University Wexner Medical Center. | Exploring Semi-Supervised Coreference Resolution of Medical Concepts using Semantic and Temporal Features |
d13203069 | Named Entity Recognition (NER) is always limited by its lower recall resulting from the asymmetric data distribution where the NONE class dominates the entity classes. This paper presents an approach that exploits non-local information to improve the NER recall. Several kinds of non-local features encoding entity token occurrence, entity boundary and entity class are explored under Conditional Random Fields (CRFs) framework. Experiments on SIGHAN 2006 MSRA (CityU) corpus indicate that non-local features can effectively enhance the recall of the state-of-the-art NER systems. Incorporating the non-local features into the NER systems using local features alone, our best system achieves a 23.56% (25.26%) relative error reduction on the recall and 17.10% (11.36%) relative error reduction on the F1 score; the improved F1 score 89.38% (90.09%) is significantly superior to the best NER system with F1 of 86.51% (89.03%) participated in the closed track. | Using Non-Local Features to Improve Named Entity Recognition Recall * |
d189898467 | In this work, we train an Automatic Post-Editing (APE) model and use it to reveal biases in standard Machine Translation (MT) evaluation procedures. The goal of our APE model is to correct typical errors introduced by the translation process, and convert the "translationese" output into natural text. Our APE model is trained entirely on monolingual data that has been round-trip translated through English, to mimic errors that are similar to the ones introduced by NMT. We apply our model to the output of existing NMT systems, and demonstrate that, while the human-judged quality improves in all cases, BLEU scores drop with forward-translated test sets. We verify these results for the WMT18 English→German, WMT15 English→French, and WMT16 English→Romanian tasks. Furthermore, we selectively apply our APE model on the output of the top submissions of the most recent WMT evaluation campaigns. We see quality improvements on all tasks of up to 2.5 BLEU points. | APE at Scale and its Implications on MT Evaluation Biases |
d248780372 | Personalized news recommendation is an essential technique to help users find interested news. Accurately matching user's interests and candidate news is the key to news recommendation. Most existing methods learn a single user embedding from user's historical behaviors to represent the reading interest. However, user interest is usually diverse and may not be adequately modeled by a single user embedding. In this paper, we propose a poly attention scheme to learn multiple interest vectors for each user, which encodes the different aspects of user interest. We further propose a disagreement regularization to make the learned interests vectors more diverse. Moreover, we design a category-aware attention weighting strategy that incorporates the news category information as explicit interest signals into the attention mechanism. Extensive experiments on the MIND news recommendation benchmark demonstrate that our approach significantly outperforms existing state-of-the-art methods. | MINER: Multi-Interest Matching Network for News Recommendation |
d1828228 | This paper presents an adaptive model of multimodal social behavior for embodied conversational agents. The context of this research is the training of youngsters for job interviews in a serious game where the agent plays the role of a virtual recruiter. With the proposed model the agent is able to adapt its social behavior according to the anxiety level of the trainee and a predefined difficulty level of the game. This information is used to select the objective of the system (to challenge or comfort the user), which is achieved by selecting the complexity of the next question posed and the agent's verbal and non-verbal behavior. We have carried out a perceptive study that shows that the multimodal behavior of an agent implementing our model successfully conveys the expected social attitudes. | A model to generate adaptive multimodal job interviews with a virtual recruiter |
d44068699 | This paper describes the SemEval 2018 Task 10 on Capturing Discriminative Attributes.Participants were asked to identify whether an attribute could help discriminate between two concepts. For example, a successful system should determine that urine is a discriminating feature in the word pair kidney,bone. The aim of the task is to better evaluate the capabilities of state of the art semantic models, beyond pure semantic similarity. The task attracted submissions from 21 teams, and the best system achieved a 0.75 F1 score. | SemEval-2018 Task 10: Capturing Discriminative Attributes |
d53224084 | Satire has played a role in indirectly expressing critique towards an authority or a person from time immemorial. We present an autonomously creative masterapprentice approach consisting of a genetic algorithm and an NMT model to produce humorous and culturally apt satire out of movie titles automatically. Furthermore, we evaluate the approach in terms of its creativity and its output. We provide a solid definition for creativity to maximize the objectiveness of the evaluation. | A Master-Apprentice Approach to Automatic Creation of Culturally Satirical Movie Titles |
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d11016000 | Knowledge graph (KG) embedding techniques use structured relationships between entities to learn lowdimensional representations of entities and relations. One prominent goal of these approaches is to improve the quality of knowledge graphs by removing errors and adding missing facts. Surprisingly, most embedding techniques have been evaluated on benchmark datasets consisting of dense and reliable subsets of human-curated KGs, which tend to be fairly complete and have few errors. In this paper, we consider the problem of applying embedding techniques to KGs extracted from text, which are often incomplete and contain errors. We compare the sparsity and unreliability of different KGs and perform empirical experiments demonstrating how embedding approaches degrade as sparsity and unreliability increase. | Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short |
d12877573 | In this paper we describe SoMaJo, a rulebased tokenizer for German web and social media texts that was the best-performing system in the EmpiriST 2015 shared task with an average F 1 -score of 99.57. We give an overview of the system and the phenomena its rules cover, as well as a detailed error analysis. The tokenizer is available as free software. | SoMaJo: State-of-the-art tokenization for German web and social media texts |
d6537905 | We propose a method for cross-language identification of semantic relations based on word similarity measurement and morphosemantic relations in WordNet. We transfer these relations to pairs of derivationally unrelated words and train a model for automatic classification of new instances of (morpho)semantic relations in context based on the existing ones and the general semantic classes of collocated verb and noun senses. Our experiments are based on Bulgarian-English parallel and comparable texts but the method is to a great extent language-independent and particularly suited to less-resourced languages, since it does not need parsed or semantically annotated data. The application of the method leads to an increase in the number of discovered semantic relations by 58.35% and performs relatively consistently, with a small decrease in precision between the baseline (based on morphosemantic relations identified in wordnet) -0.774, and the extended method (based on the data obtained through machine learning) -0.721. | Wordnet-Based Cross-Language Identification of Semantic Relations |
d19914801 | 不同母語背景華語學習者的用詞特徵： 以語料庫為本的研究 * Salient Linguistic Features of Chinese Learners with Different L1s: A Corpus-based Study | |
d2955949 | EBERHARD PAUSE A CLASS OF TRANSFOKMATIONAL RECOGNITION GRAMMARS | |
d691418 | In this paper, we present an extension of the DCR method, which is a framework for the deep evaluation of Spoken Language Understanding (SLU) Systems. The key point of our contribution is the use of a linguistic typology in order to generate an evaluation corpus that covers a significant number of the linguistic phenomena we want to evaluate our system on. This allows to have more objective and deep evaluation of SLU systems. | Toward an objective and generic Method for Spoken Language Understanding Systems Evaluation: an extension of the DCR method |
d237010892 | This paper addresses the tasks of sign segmentation and segment-meaning mapping in the context of sign language (SL) recognition. It aims to give an overview of the linguistic properties of SL, such as coarticulation and simultaneity, which make these tasks complex. A better understanding of SL structure is the necessary ground for the design and development of SL recognition and segmentation methodologies, which are fundamental for machine translation of these languages. Based on this preliminary exploration, a proposal for mapping segments to meaning in the form of an agglomerate of lexical and non-lexical information is introduced. | Defining meaningful units. Challenges in sign segmentation and segment-meaning mapping |
d7710946 | We propose a method of revising lead sentences in a news broadcast. Unlike many other methods proposed so far, this method does not use the coreference relation of noun phrases (NPs) but rather, insertion and substitution of the phrases modifying the same head chunk in lead and other sentences. The method borrows an idea from the sentence fusion methods and is more general than those using NP coreferencing as ours includes them. We show in experiments the method was able to find semantically appropriate revisions thus demonstrating its basic feasibility. We also show that that parsing errors mainly degraded the sentential completeness such as grammaticality and redundancy. | Syntax-Driven Sentence Revision for Broadcast News Summarization |
d9769888 | We present an unsupervised extraction of sequence-to-sequence correspondences from parallel corpora by sequential pattern mining. The main characteristics of our method are two-fold. First, we propose a systematic way to enumerate all possible translation pair candidates of rigid and gapped sequences without falling into combinatorial explosion. Second, our method uses an efficient data structure and algorithm for calculating frequencies in a contingency table for each translation pair candidate. Our method is empirically evaluated using English-Japanese parallel corpora of 6 million words. Results indicate that it works well for multi-word translations, giving 56-84% accuracy at 19% token coverage and 11% type coverage.Our Basic IdeaOur approach is illustrated inFigure 1. We concatenate corresponding parallel sentences into bilingual sequences to which sequential pattern mining is applied. By doing so, we obtain the following effects:• It exhaustively generates all possible translation can-1 As of this writing, we learn that Moore will present his results on named entity at EACL 2003. | Learning Sequence-to-Sequence Correspondences from Parallel Corpora via Sequential Pattern Mining |
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d7858698 | Traditional text categorization is usually a topic-based task, but a subtle demand on information retrieval is to distinguish between positive and negative view on text topic. In this paper, a new method is explored to solve this problem. Firstly, a batch of Concerned Concepts in the researched domain is predefined. Secondly, the special knowledge representing the positive or negative context of these concepts within sentences is built up. At last, an evaluating function based on the knowledge is defined for sentiment classification of free text. We introduce some linguistic knowledge in these procedures to make our method effective. As a result, the new method proves better compared with SVM when experimenting on Chinese texts about a certain topic. | A New Method for Sentiment Classification in Text Retrieval |
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d3832329 | This paper presents a deep linguistic attentional framework which incorporates word level concept information into neural classification models. While learning neural classification models often requires a large amount of labelled data, linguistic concept information can be obtained from external knowledge, such as pre-trained word embeddings, WordNet for common text and MetaMap for biomedical text. We explore two different ways of incorporating word level concept annotations, and show that leveraging concept annotations can boost the model performance and reduce the need for large amounts of labelled data. Experiments on various data sets validate the effectiveness of the proposed method. | Leveraging linguistic resources for improving neural text classification |
d252212914 | maria.gritz@yandex.ru | |
d8051993 | This paper presents the semi-automatic construction method of a practical ontology by using various resources. In order to acquire a reasonably practical ontology in a limited time and with less manpower, we extend the Kadokawa thesaurus by inserting additional semantic relations into its hierarchy, which are classified as case relations and other semantic relations. The former can be obtained by converting valency information and case frames from previously-built computational dictionaries used in machine translation. The latter can be acquired from concept co-occurrence information, which is extracted automatically from large corpora. The ontology stores rich semantic constraints among 1,110 concepts, and enables a natural language processing system to resolve semantic ambiguities by making inferences with the concept network of the ontology. In our practical machine translation system, our ontology-based word sense disambiguation method achieved an 8.7% improvement over methods which do not use an ontology for Korean translation. | Semi-Automatic Practical Ontology Construction by Using a Thesaurus, Computational Dictionaries, and Large Corpora |
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d259376811 | In this paper, we present a possible solution to the SemEval23 shared task of generating spoilers for clickbait headlines. Using a Zero-Shot approach with two different Transformer architectures, BLOOM and RoBERTa, we generate three different types of spoilers: phrase, passage and multi. We found, RoBERTa pretrained for Question-Answering to perform better than BLOOM for causal language modelling, however both architectures proved promising for future attempts at such tasks. | |
d259376535 | We introduce the Korean-Learner-Morpheme (KLM) corpus, a manually annotated dataset consisting of 129,784 morphemes from second language (L2) learners of Korean, featuring morpheme tokenization and part-of-speech (POS) tagging. We evaluate the performance of four Korean morphological analyzers in tokenization and POS tagging on the L2-Korean corpus. Results highlight the analyzers' reduced performance on L2 data, indicating the limitation of advanced deep-learning models when dealing with L2-Korean corpora. We further show that fine-tuning one of the models with the KLM corpus improves its accuracy of tokenization and POS tagging on L2-Korean dataset. | Towards L2-friendly pipelines for learner corpora: A case of written production by L2-Korean learners |
d259376561 | The task ValueEval aims at assigning a subset of possible human value categories underlying a given argument. Values behind arguments are often determinants to evaluate the relevance and importance of decisions in ethical sense, thereby making them essential for argument mining. The work presented here proposes two systems for the same. Both systems use RoBERTa to encode sentences in each document. System1 makes use of features obtained from training models for two auxiliary tasks, whereas System2 combines RoBERTa with topic modeling to get sentence representation. These features are used by a classification head to generate predictions. System1 secured the rank 22 in the official task ranking, achieving the macro F1-score 0.46 on the main dataset. System2 was not a part of official evaluation. Subsequent experiments achieved highest (among the proposed systems) macro F1-scores of 0.48 (System2), 0.31 (ablation on System1) and 0.33 (ablation on System1) on the main dataset, the Nahj al-Balagha dataset, and the New York Times dataset. | LRL_NC at SemEval-2023 Task 4: The Touche23-george-boole Approach for Multi-Label Classification of Human-Values Behind Arguments |
d259376677 | Based on recent progress in image-text retrieval techniques, this paper presents a fine-tuned model for the Visual Word Sense Disambiguation (VWSD) task. The proposed system finetunes a pre-trained model using ITC and ITM losses and employs a candidate selection approach for faster inference. The system was trained on the VWSD task dataset and evaluated on a separate test set using Mean Reciprocal Rank (MRR) metric. Additionally, the system was tested on the provided test set which contained Persian and Italian languages, and the results were evaluated on each language separately. Our proposed system demonstrates the potential of fine-tuning pre-trained models for complex language tasks and provides insights for further research in the field of image text retrieval. | SLT at SemEval-2023 Task 1: Enhancing Visual Word Sense Disambiguation through Image Text Retrieval using BLIP |
d14264106 | This paper presents our machine transliteration systems developed for the NEWS 2015 machine transliteration shared task. Our systems are applied to two tasks: English to Chinese and Chinese to English. For standard runs, in which only official data sets are used, we build phrase-based transliteration models with refined alignments provided by the M2M-aligner. For non-standard runs, we add multilingual resources to the systems designed for the standard runs and build different language specific transliteration systems. Linear regression is adopted to rerank the outputs afterwards, which significantly improves the overall transliteration performance. | Boosting English-Chinese Machine Transliteration via High Quality Alignment and Multilingual Resources |
d28001127 | COMPONENTS OF SEMANTIC REPRESENTATION | |
d205486 | The transliteration has attracted interest of several sections of researchers. Several techniques of transliteration have been developed and usedboth statistical based approaches and rule based approaches. In the present method, a simple but effective rule based technique is developed for the transliteration between Bengali script and Meetei Mayek script of written Manipuri text. Typically, transliteration is carried out between two different languages -one as a source and the other as a target. But, for the languages which use more than one script, it becomes essential to introduce transliteration between the scripts. This is the reason why the present task is carried out between Bengali script and Meetei Mayek for Manipuri language. The proposed rule based approach points out the importance of deeper linguistic rule integration in the process by making use of the monosyllabic characteristics of Manipuri language. The Bengali script to MeeteiMayek transliteration system based on the proposed model gives higher precision and recall compared to the statistical model. But, in contrast to that, the statistical based approach gives higher precision and recall compared to the rule based approach for the reverse transliteration. | Bidirectional Bengali Script and Meetei Mayek Transliteration of Web Based Manipuri News Corpus |
d22121313 | There is a growing demand for translation. To meet this demand, many translation companies are introducing a hybrid technology solution combining translation memory and machine translation. However, few trainee translators receive training in machine translation postediting. This paper asks the question: Why should translator training programmes teach post-editing skills? Is post-editing the same as translation and traditional revision? The skillsets required of a post-editor are listed and the usual list of skills is extended. An outline for a course in post-editing, divided into theoretical and practical components, is proposed. Finally, the question of when such a course should be given to trainee translators is addressed. | Teaching Post-editing: A Proposal for Course Content |
d26565480 | Even with recent, renewed attention to MT evaluation-due in part to n-gram-based metrics(Papineni et al., 2001;Doddington, 2002)and the extensive, online catalogue of MT metrics on the ISLE project(Hovy et al., 2001, few reports involving task-based metrics have surfaced. This paper presents our work on three parts of taskbased MT evaluation: (i) software to track and record users' task performance via a browser, run from a desktop computer or remotely over the web, (ii) factorial experimental design with replicate observations to compare the MT engines, based on the accuracy of users' task responses, and (iii) the use of chi-squared and generalized linear models (GLMs) to permit finer-grained data analyses. We report on the experimental results of a six-way document categorization task, used for the evaluation of three Korean-English MT engines. The statistical models of the probabilities of correct responses yield an ordering of the MT engines, with one engine having a statistically significant lead over the other two. Future research will involve testing user performance on linguistically more complex tasks, as well as extending our initial GLMs with the documents' Bleu scores as variables, to test the scores as independent predictors of task results. | Task-based MT Evaluation: Tackling Software, Experimental Design, & Statistical Models |
d19099953 | This paper proposes a new methodology in investigating the semantic and pragmatic properties of SFPs in Mandarin Chinese. A case study of the interaction and correlation between SFP-Ne and SpOAs--Shenzhi, Qishi, and Nanguai has been conducted. Two semantic features of [+unexpectedness] and [+intersubjectivity] have been summarized on SFP-Ne. | The Interaction between SFPs and Adverbs in Mandarin Chinese -A Corpus-Based Approach |
d28651080 | After celebrating its 20th anniversary in 2015, ELRA is carrying on its strong involvement in the HLT field. To share ELRA's expertise of those 21 past years, this article begins with a presentation of ELRA's strategic Data and LR Management Plan for a wide use by the language communities. Then, we further report on ELRA's activities and services provided since LREC 2014. When looking at the cataloguing and licensing activities, we can see that ELRA has been active at making the Meta-Share repository move toward new developments steps, supporting Europe to obtain accurate LRs within the Connecting Europe Facility programme, promoting the use of LR citation, creating the ELRA License Wizard web portal. The article further elaborates on the recent LR production activities of various written, speech and video resources, commissioned by public and private customers. In parallel, ELDA has also worked on several EU-funded projects centred on strategic issues related to the European Digital Single Market. The last part gives an overview of the latest dissemination activities, with a special focus on the celebration of its 20 th anniversary organised in Dubrovnik (Croatia) and the following up of LREC, as well as the launching of the new ELRA portal. | ELRA Activities and Services |
d21720516 | Chinese is a language rich in nonlocal dependencies. Correctly resolving these dependencies is crucial in understanding the predicateargument structure of a sentence. Making full use of the trace annotations in the Penn Chinese Treebank(Xue et al., 2005), this research contributes several test sets of Chinese nonlocal dependencies which occur in different grammatical constructions. These datasets can be used by an automatic dependency parser to evaluate its performance on nonlocal dependency resolution in various syntactic constructions in Chinese. | Test Sets for Chinese Nonlocal Dependency Parsing |
d243865576 | Data augmentation and adversarial perturbation approaches have recently achieved promising results in solving the over-fitting problem in many natural language processing (NLP) tasks including sentiment classification. However, existing studies aimed to improve the generalization ability by augmenting the training data with synonymous examples or adding random noises to word embeddings, which cannot address the spurious association problem. In this work, we propose an end-toend reinforcement learning framework, which jointly performs counterfactual data generation and dual sentiment classification. Our approach has three characteristics: 1) the generator automatically generates massive and diverse antonymous sentences; 2) the discriminator contains a original-side sentiment predictor and an antonymous-side sentiment predictor, which jointly evaluate the quality of the generated sample and help the generator iteratively generate higher-quality antonymous samples; 3) the discriminator is directly used as the final sentiment classifier without the need to build an extra one. Extensive experiments show that our approach outperforms strong data augmentation baselines on several benchmark sentiment classification datasets. Further analysis confirms our approach's advantages in generating more diverse training samples and solving the spurious association problem in sentiment classification. | Reinforced Counterfactual Data Augmentation for Dual Sentiment Classification |
d16157451 | This paper investigates the influences of three different reading styles (Lyric, Critical and Explanatory) to the distribution tendency of sentential accents (classified as rhythmic accent and semantic accent). The comparison among multiple styles is performed in three research domains: high-level constructions, low-level phrases and disyllabic prosodic words. One finds that the assignment of semantic accents shows some differences across reading styles, while the assignment of rhythmic accents does not. Furthermore, the larger the speech unit studied, the stronger the influence is observed, i.e. most differences in the assignment of semantic accents are shown in high-level constructions, some are shown in low-level phrases, and none are shown in prosodic words across the three reading styles.Compared with previous studies, the allocation scheme of semantic accents in the Explanatory style is close to that in the neutral style, i.e. in high-level constructions, it has a final-accented tendency in theme + rheme (TR), predicate + object(PO) and subject + predicate(SP) constructions, and uniform distribution in adjunct + head constructions. In low-level phrases, the Explanatory style exhibits an initial-accented tendency in adjunct + head phrases, but a final-accented tendency in subject + predicate (SP) phrases and predicate + object (PO) phrase.The Critical style is adopted to make comments, where semantic focal points are normally on the core subjects and their actions. As a result, more accents are allocated to the subject part in the AS constructions and to the predicate part in the PO constructions. Accordingly, in low-level phrases, more accents go to the heads 1 The work was carried out as an intern in Microsoft Research Asia.92Mingzhen Bao et al.in AN phrases and the predicates in SP phrases. The Lyric style helps to express personal emotions in a rhythmic way[Wang 2000]. Such poetry-like rhythm weakens the effect of syntactic constrains, and in many cases, leads to an even distribution of semantic accents in high-level constructions and dense distribution near prosodic boundaries. | The Influence of Reading Styles on Accent Assignment in Mandarin 1 |
d251498131 | In this work, we conduct a quantitative linguistic analysis of the language usage patterns of multilingual peer supporters in two health-focused WhatsApp groups in Kenya comprising of youth living with HIV. Even though the language of communication for the group was predominantly English, we observe frequent use of Kiswahili, Sheng and code-mixing among the three languages. We present an analysis of language choice and its accommodation, different functions of code-mixing, and relationship between sentiment and code-mixing. To explore the effectiveness of off-the-shelf Language Technologies (LT) in such situations, we attempt to build a sentiment analyzer for this dataset. Our experiments demonstrate the challenges of developing LT and therefore effective interventions for such forums and languages. We provide recommendations for language resources that should be built to address these challenges. | Language Patterns and Behaviour of the Peer Supporters in Multilingual Healthcare Conversational Forums |
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d15616495 | In distributional semantics studies, there is a growing attention in compositionally determining the distributional meaning of word sequences. Yet, compositional distributional models depend on a large set of parameters that have not been explored. In this paper we propose a novel approach to estimate parameters for a class of compositional distributional models: the additive models. Our approach leverages on two main ideas. Firstly, a novel idea for extracting compositional distributional semantics examples. Secondly, an estimation method based on regression models for multiple dependent variables. Experiments demonstrate that our approach outperforms existing methods for determining a good model for compositional distributional semantics. | Estimating Linear Models for Compositional Distributional Semantics |
d258463933 | The paper proposes "Critical Presuppositions-To-Theory" (C-PRETTY) research as a new form of qualitative inquiry due to the realized challenges of grounded theory and the seemingly barren domain of theory building. The C-PRETTY serves as a special type of qualitative inquiry that focuses on critical presuppositional framework and critical presuppositions as the primary theoretical and methodological compass during the zigzagging data gathering and analysis. Guided by these, observations of two English classrooms, interviews with their teacher, and focus group discussions with the students were done to investigate classroom interaction with the belief that there is an exigency to create a contextualized theory to better depict the contextual realities of meaningmaking as an integral part of classroom learning. The theory generated has been formalized as the Multi-Layered Symbiotic Process of Meaning-Making, which proposes that meaning-making starts at an Interactional Reference Point (IRP) and is carried through five mediums: locutionary, kinesic, affective-prosodic, cultural, and physical-spatial. These mediums interact with the intermediary layers as meaning travels through them. The findings of this study bring forth new ways of theorizing and conducting investigations, especially in the realm of educational linguistics. | Theorizing on Meaning-Making in Classroom Interaction Using "Critical Presuppositions-To-Theory" (C-PRETTY) Research |
d28714777 | Qualitative and Quantitative Dynamics of .Vowels | |
d855149 | Noun phrases (NPs) are a crucial part of natural language, and can have a very complex structure. However, this NP structure is largely ignored by the statistical parsing field, as the most widely used corpus is not annotated with it. This lack of gold-standard data has restricted previous efforts to parse NPs, making it impossible to perform the supervised experiments that have achieved high performance in so many Natural Language Processing (NLP) tasks.We comprehensively solve this problem by manually annotating NP structure for the entire Wall Street Journal section of the Penn Treebank. The inter-annotator agreement scores that we attain dispel the belief that the task is too difficult, and demonstrate that consistent NP annotation is possible. Our gold-standard NP data is now available for use in all parsers.We experiment with this new data, applying the Collins(2003)parsing model, and find that its recovery of NP structure is significantly worse than its overall performance. The parser's F-score is up to 5.69% lower than a baseline that uses deterministic rules. Through much experimentation, we determine that this result is primarily caused by a lack of lexical information.To solve this problem we construct a wide-coverage, large-scale NP Bracketing system. With our Penn Treebank data set, which is orders of magnitude larger than those used previously, we build a supervised model that achieves excellent results. Our model performs at 93.8% F-score on the simple NP task that most previous work has undertaken, and extends to bracket longer, more complex NPs that are rarely dealt with in the literature. We attain 89.14% F-score on this much more difficult task. Finally, we implement a post-processing module that brackets NPs identified by theBikel (2004)parser. Our NP Bracketing model includes a wide variety of features that provide the lexical information that was missing during the parser experiments, and as a result, we outperform the parser's F-score by 9.04%.These experiments demonstrate the utility of the corpus, and show that many NLP applications can now make use of NP structure. Computational Linguistics Volume 37, Number 4 Computational Linguistics Volume 37, Number 4NP, to which we can add modifiers and determiners to form a saturated NP. Or, in terms of X-bar theory, the head is an N-bar, as opposed to the fully formed NP. Modifiers do not raise the level of the N-bar, allowing them to be added indefinitely, whereas determiners do, making NPs such as *the the dog ungrammatical.The Penn Treebank annotates at the NP level, but leaves much of the N-bar level structure unspecified. As a result, most of the structure we annotate will be on unsaturated NPs. There are some exceptions to this, such as appositional structure, where we bracket the saturated NPs being apposed. Quirk et al. (1985, §17.2) describe the components of a noun phrase as follows: | Parsing Noun Phrases in the Penn Treebank |
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d14399076 | A Probabilistic Recursive Transition Network is an elevated version of a Recursive Transition Network used to model and process context-free languages in stochastic parameters. We present a re-estimation algorithm for training probabilistic parameters, and show how efficiently it can be implemented using charts. The complexity of the Outside algorithm we present is O(N4G 3) where N is the input size and G is the number of states. This complexity can be significantly overcome when the redundant computations are avoided. Experiments on the Penn tree corpus show that re-estimation can be done more efficiently with charts. | A Chart Re-estimation Algorithm for a Probabilistic Recursive Transition Network |
d39109418 | We present a systematic analysis of lexicalized vs. delexicalized parsing in lowresource scenarios, and propose a methodology to choose one method over another under certain conditions. We create a set of simulation experiments on 41 languages and apply our findings to 9 lowresource languages. Experimental results show that our methodology chooses the best approach in 8 out of 9 cases. | Lexicalized vs. Delexicalized Parsing in Low-Resource Scenarios |
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d147238304 | Les utilisateurs d'un système de recherche d'information mettent en oeuvre des comportements de recherche complexes tels que la reformulation de requête et la recherche multitâche afin de satisfaire leurs besoins d'information. Ces comportements de recherche peuvent être observés à travers des journaux de requêtes, et constituent des indices permettant une meilleure compréhension des besoins des utilisateurs. Dans cette perspective, il est nécessaire de regrouper au sein d'une même session de recherche les requêtes reliées à un même besoin d'information. Nous proposons une méthode de détection automatique des sessions exploitant la collection de documents WIKIPÉDIA, basée sur la similarité des résultats renvoyés par l'interrogation de cette collection afin d'évaluer la similarité entre les requêtes. Cette méthode obtient de meilleures performances que les approches temporelle et lexicale traditionnellement employées pour la détection de sessions séquentielles, et peut être appliquée à la détection de sessions imbriquées. Ces expérimentations ont été réalisées sur des données provenant du portail OpenEdition.ABSTRACTAutomatic search session detection exploiting results similarity from an external document collectionSearch engines users apply complex search behaviours such as query reformulation and multitasking search to satisfy their information needs. These search behaviours may be observed through query logs, and constitute clues allowing a better understanding of users' needs. In this perspective, it is decisive to group queries related to the same information need into a unique search session. We propose an automatic session detection method exploiting the WIKIPEDIA documents collection, based on the similarity between the results returned for each query pair to estimate the similarity between queries. This method shows better performance than both temporal and lexical approaches traditionally used for successive session detection, and can be applied as well to multitasking search session detection. These experiments were conducted on a dataset originating from the OpenEdition Web portal. MOTS-CLÉS : Recherche d'information, détection automatique de sessions de recherche, analyse de journal de requêtes. | Détection automatique des sessions de recherche par similarité des résultats provenant d'une collection de documents externe |
d8102441 | This paper presents a Constraint Grammarinspired machine learner and parser, Ling Pars, that assigns dependencies to morpho logically annotated treebanks in a functioncentred way. The system not only bases at tachment probabilities for PoS, case, mood, lemma on those features' function probabili ties, but also uses topological features like function/PoS n-grams, barrier tags and daughter-sequences. In the CoNLL shared task, performance was below average on at tachment scores, but a relatively higher score for function tags/deprels in isolation suggests that the system's strengths were not fully exploited in the current architecture. | LingPars, a Linguistically Inspired, Language-Independent Machine Learner for Dependency Treebanks |
d1166608 | In some applications of opinion mining in text, it is important to distinguish what an author is talking about from the subjective stance towards the topic. Therefore, it needs to find the relation between sentiment expression and target. This paper proposes a novel method based on dependency grammars to mine the relation. In this method, the process of mining the relations is turned into a procedure of searching in the dependency tree of a sentence. The result of our experiments shows that word dependency relation based methods is more flexible and effective than some previous surface patterns based methods. | Mining the Relation between Sentiment Expression and Target Using Dependency of Words |
d14086043 | We define the task of incremental or 0lag utterance segmentation, that is, the task of segmenting an ongoing speech recognition stream into utterance units, and present first results. We use a combination of hidden event language model, features from an incremental parser, and acoustic / prosodic features to train classifiers on real-world conversational data (from the Switchboard corpus). The best classifiers reach an F-score of around 56%, improving over baseline and related work. | Coling 2008: Companion volume -Posters and Demonstrations |
d226262307 | wikiHow is a resource of how-to guides that describe the steps necessary to accomplish a goal. Guides in this resource are regularly edited by a community of users, who try to improve instructions in terms of style, clarity and correctness. In this work, we test whether the need for such edits can be predicted automatically. For this task, we extend an existing resource of textual edits with a complementary set of approx. 4 million sentences that remain unedited over time and report on the outcome of two revision modeling experiments. | Towards Modeling Revision Requirements in wikiHow Instructions |
d20217738 | Plans, Inference, and Indirect Speech Acts I | |
d54001605 | Domain adaptation remains one of the most challenging aspects in the wide-spread use of Semantic Role Labeling (SRL) systems. Current state-of-the-art methods are typically trained on large-scale datasets, but their performances do not directly transfer to lowresource domain-specific settings. In this paper, we propose two approaches for domain adaptation in biological domain that involve pre-training LSTM-CRF based on existing large-scale datasets and adapting it for a low-resource corpus of biological processes. Our first approach defines a mapping between the source labels and the target labels, and the other approach modifies the final CRF layer in sequence-labeling neural network architecture. We perform our experiments on Pro-cessBank (Berant et al., 2014) dataset which contains less than 200 paragraphs on biological processes. We improve over the previous state-of-the-art system on this dataset by 21 F1 points. We also show that, by incorporating event-event relationship in ProcessBank, we are able to achieve an additional 2.6 F1 gain, giving us possible insights into how to improve SRL systems for biological process using richer annotations. | Domain Adaptation of SRL Systems for Biological Processes |
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d16362279 | African American English (AAE) is a well-established dialect that exhibits a distinctive syntax, including constructions like habitual be. Using data mined from the social media service Twitter, the proposed senior thesis project intends to study the demographic distribution of a subset of AAE syntactic constructions. This study expands on previous sociolinguistic Twitter work by adding part-of-speech tags to the data, thus enabling detection of short-range syntactic features. Through an analysis of ethnic and gender data associated with AAE tweets, this project will provide a more accurate description of the dialect's speakers and distribution. | Now We Stronger Than Ever: African-American Syntax in Twitter |
d16482037 | In the period since 2004, many novel sophisticated approaches for generic multi-document summarization have been developed. Intuitive simple approaches have also been shown to perform unexpectedly well for the task. Yet it is practically impossible to compare the existing approaches directly, because systems have been evaluated on different datasets, with different evaluation measures, against different sets of comparison systems. Here we present a corpus of summaries produced by several state-of-the-art extractive summarization systems or by popular baseline systems. The inputs come from the 2004 DUC evaluation, the latest year in which generic summarization was addressed in a shared task. We use the same settings for ROUGE automatic evaluation to compare the systems directly and analyze the statistical significance of the differences in performance. We show that in terms of average scores the state-of-the-art systems appear similar but that in fact they produce very different summaries. Our corpus will facilitate future research on generic summarization and motivates the need for development of more sensitive evaluation measures and for approaches to system combination in summarization. | A Repository of State of the Art and Competitive Baseline Summaries for Generic News Summarization |
d250390494 | The recent transition to the online educational domain has increased the need for Automatic Short Answer Grading (ASAG). ASAG automatically evaluates a student's response against a (given) correct response and thus has been a prevalent semantic matching task. Most existing methods utilize sequential context to compare two sentences and ignore the structural context of the sentence; therefore, these methods may not result in the desired performance. In this paper, we overcome this problem by proposing a Multi-Relational Graph Transformer, MitiGaTe, to prepare token representations considering the structural context. Abstract Meaning Representation (AMR) graph is created by parsing the text response and then segregated into multiple subgraphs, each corresponding to a particular relationship in AMR. A Graph Transformer is used to prepare relation-specific token embeddings within each subgraph, then aggregated to obtain a subgraph representation. Finally, we compare the correct answer and the student response subgraph representations to yield a final score. Experimental results on Mohler's dataset show that our system outperforms the existing stateof-the-art methods. We have released our implementation 1 , as we believe that our model can be useful for many future applications. † | Multi-Relational Graph Transformer for Automatic Short Answer Grading † |
d16286541 | This paper proposes a Japanese opinion word translation method based on unsupervised word sense disambiguation. The method comprises the corpus preparation, opinion word dictionary construction, and weighting method. Different from the machine translation, our method does not need parallel corpora, tagged corpora or parsing tree banks. Our method is low-cost but effective, and requires a well-made bilingual dictionary only. Besides, our method can extract key information from the opinions to help users understand the opinions. We construct four configurations and evaluate our method on four Japanese opinion words with high frequency. The evaluation result shows that the dependency grammar and opinion word dictionary is effective on opinion word translation. Our method can deal with the translation disambiguation problem and improve the translation precision to help user realize Japanese opinions. 關鍵詞：詞義消歧，意見詞，字詞翻譯，非監督式 Keywords: Word Sense Disambiguation, Opinion Word, Word Translation, Unsupervised 一、緒論 隨著網路的蓬勃發展，越來越多的使用者會在網路上發表對產品的評論，或者對於美食 157 | 基於非監督式詞義消歧之日語旅遊意見詞翻譯 Japanese Opinion Word Translation Based on Unsupervised Word Sense Disambiguation in the Travel Domain |
d5730387 | This paper introduces a tensor-based approach to semantic role labeling (SRL). The motivation behind the approach is to automatically induce a compact feature representation for words and their relations, tailoring them to the task. In this sense, our dimensionality reduction method provides a clear alternative to the traditional feature engineering approach used in SRL. To capture meaningful interactions between the argument, predicate, their syntactic path and the corresponding role label, we compress each feature representation first to a lower dimensional space prior to assessing their interactions. This corresponds to using an overall cross-product feature representation and maintaining associated parameters as a four-way low-rank tensor. The tensor parameters are optimized for the SRL performance using standard online algorithms. Our tensor-based approach rivals the best performing system on the CoNLL-2009 shared task. In addition, we demonstrate that adding the representation tensor to a competitive tensorfree model yields 2% absolute increase in Fscore. 1 | High-Order Low-Rank Tensors for Semantic Role Labeling |
d52139395 | With the rapid progress of computer technology and world-wide development of information networks, a vast amount of information is now being generated, published, and stored at a number of sites distributed all over the world. Such an affluence of information, however, is useless or may even become harmful unless one has a means for rapidly retrieving the information that it truly necessary and appropriate. Conventional systems for information retrieval, however, are not always easy to use for inexperienced users, and are neither efficient nor accurate. In many cases, it is difficult for the user to identify and express his/her intention precisely, and it is difficult also for the system to infer the user's intention correctly. These difficulties can be alleviated by introducing spoken dialogue between the user and the system. Furthermore, in conventional systems using keywords, the accuracy of retrieval is reduced by the existence of synonymy, polysemy and homonymy, as well as of unknown words. Still another shortcoming of conventional systems is the lack of ability for properly estimating the degree of relevance of a document to the user's query, as well as the lack of a proper viewpoint on the cost/performance of retrieval.This talk describes the outcome of a successful Japanese national project conducted under the "Research-for-the-Future" program and led by the speaker as the principal investigator. The system is based on the following three original principles: (a) Dialogue Management based on both User and System Modeling (by introducing a novel type of interacting automaton), (b) Use of "Key Concepts" in information retrieval (including processing of polysemy, homonymy, and unknown words), and (c) Optimization of Retrieval Performance through Relevance Score Estimation (by introducing a measure of relevance of search results based on users' judgments. The advantages of these novel principles have been demonstrated by a pilot system. | International Speech Communication Association Distinguished Lecture: Principles and Design of a System for Academic Information Retrieval based on Human-Machine Dialogue |
d367308 | This paper presents a method to improve a word alignment model in a phrase-based Statistical Machine Translation system for a lowresourced language using a string similarity approach. Our method captures similar words that can be seen as semi-monolingual across languages, such as numbers, named entities, and adapted/loan words. We use several string similarity metrics to measure the monolinguality of the words, such as Longest Common Subsequence Ratio (LCSR), Minimum Edit Distance Ratio (MEDR), and we also use a modified BLEU Score (modBLEU).Our approach is to add intersecting alignment points for word pairs that are orthographically similar, before applying a word alignment heuristic, to generate a better word alignment.We demonstrate this approach on Indonesianto-English translation task, where the languages share many similar words that are poorly aligned given a limited training data. This approach gives a statistically significant improvement by up to 0.66 in terms of BLEU score. | Improving Word Alignment by Exploiting Adapted Word Similarity |
d466973 | When applying text classification to complex tasks, it is tedious and expensive to hand-label the large amounts of training data necessary for good performance. This paper presents an alternative approach to text classification that requires no labeled documentsi instead, it uses a small set of keywords per class, a class hierarchy and a large quantity of easilyobtained unlabeled documents. The keywords are used to assign approximate labels to the unlabeled documents by termmatching. These preliminary labels become the starting point for a bootstrapping process that learns a naive Bayes classifier using Expectation-Maximization and hierarchical shrinkage. When classifying a complex data set of computer science research papers into a 70-leaf topic hierarchy, the keywords alone provide 45% accuracy. The classifier learned by bootstrapping reaches 66% accuracy, a level close to human agreement. | Text Classification by Bootstrapping with Keywords, EM and Shrinkage |
d9369670 | The paper discusses the use of corpora for experimental studies in contrastive lexical semantics, in particular, for comparing how a state of affairs is expressed in different languages and by different translators. Three topics are addressed: (1) a lexicographic database, which is aimed at storing and maintaining contrastive descriptions of a class of lexical items in several languages; (2) an aligned parallel English-Russian corpus, including several literary texts and software manuals (the total size is about one million words), together with tools for querying the corpus by means of Perl-based regular expressions; and (3) an example of development of a lexicographical database of the most frequent size adjectives in English, German and Russian. | Meaning as use: exploitation of aligned corpora for the contrastive study of lexical semantics |
d259370570 | For many languages, machine translation progress is hindered by the lack of reliable training data. Models are trained on whatever preexisting datasets may be available and then augmented with synthetic data, because it is often not economical to pay for the creation of largescale datasets. But for the case of low-resource languages, would the creation of a few thousand professionally translated sentence pairs give any benefit? In this paper, we show that it does.We describe a broad data collection effort involving around 6k professionally translated sentence pairs for each of 39 low-resource languages, which we make publicly available. We analyse the gains of models trained on this small but high-quality data, showing that it has significant impact even when larger but lower quality pre-existing corpora are used, or when data is augmented with millions of sentences through backtranslation. . 2022a. A few thousand translations go a long way! leveraging pre-trained models for African news translation. In | Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation |
d259370641 | Document ranking aims at sorting a collection of documents with their relevance to a query. Contemporary methods explore more efficient transformers or divide long documents into passages to handle the long input. However, intensive query-irrelevant content may lead to harmful distraction and high query latency. Some recent works further propose cascade document ranking models that extract relevant passages with an efficient selector before ranking, however, their selection and ranking modules are almost independently optimized and deployed, leading to selecting error reinforcement and sub-optimal performance. In fact, the document ranker can provide fine-grained supervision to make the selector more generalizable and compatible, and the selector built upon a different structure can offer a distinct perspective to assist in document ranking. Inspired by this, we propose a fine-grained attention alignment approach to jointly optimize a cascade document ranking model. Specifically, we utilize the attention activations over the passages from the ranker as fine-grained attention feedback to optimize the selector. Meanwhile, we fuse the relevance scores from the passage selector into the ranker to assist in calculating the cooperative matching representation. Experiments on MS MARCO and TREC DL demonstrate the effectiveness of our method. | FAA: Fine-grained Attention Alignment for Cascade Document Ranking |
d1613767 | While large-scale discriminative training has triumphed in many NLP problems, its definite success on machine translation has been largely elusive. Most recent efforts along this line are not scalable (training on the small dev set with features from top ∼100 most frequent words) and overly complicated. We instead present a very simple yet theoretically motivated approach by extending the recent framework of "violation-fixing perceptron", using forced decoding to compute the target derivations. Extensive phrase-based translation experiments on both Chinese-to-English and Spanish-to-English tasks show substantial gains in BLEU by up to +2.3/+2.0 on dev/test over MERT, thanks to 20M+ sparse features. This is the first successful effort of large-scale online discriminative training for MT. | Max-Violation Perceptron and Forced Decoding for Scalable MT Training |
d2375338 | Language modeling is an important part for both speech recognition and machine translation systems. Adaptation has been successfully applied to language models for speech recognition. In this paper we present experiments concerning language model adaptation for statistical machine translation. We develop a method to adapt language models using information retrieval methods. The adapted language models drastically reduce perplexity over a general language model and we can show that it is possible to improve the translation quality of a statistical machine translation using those adapted language models instead of a general language model. | Language Model Adaptation for Statistical Machine Translation based on Information Retrieval |
d13382281 | When generating utterances, humans may choose among a number of alternative sentence forms expressing the same propositional content. The context determines these decisions to a large extent. This paper presents a strategy to allow for such context-sensitive variation when generating text for a wearable, advice giving device. Several dimensions of context feed a model of the heater's attention space, which, in terms of Information Structure Theory, determines the form of the sentence to be generated. | From Context to Sentence Form |
d6278207 | We propose a technique for learning representations of parser states in transitionbased dependency parsers. Our primary innovation is a new control structure for sequence-to-sequence neural networksthe stack LSTM. Like the conventional stack data structures used in transitionbased parsing, elements can be pushed to or popped from the top of the stack in constant time, but, in addition, an LSTM maintains a continuous space embedding of the stack contents. This lets us formulate an efficient parsing model that captures three facets of a parser's state: (i) unbounded look-ahead into the buffer of incoming words, (ii) the complete history of actions taken by the parser, and (iii) the complete contents of the stack of partially built tree fragments, including their internal structures. Standard backpropagation techniques are used for training and yield state-of-the-art parsing performance. | Transition-Based Dependency Parsing with Stack Long Short-Term Memory |
d233365345 | In the last few decades, Code-Mixed Offensive texts are used penetratingly in social media posts. Social media platforms and online communities showed much interest on offensive text identification in recent years. Consequently, research community is also interested in identifying such content and also contributed to the development of corpora. Many publicly available corpora are there for research on identifying offensive text written in English language but rare for low resourced languages like Tamil. The first code-mixed offensive text for Dravidian languages are developed by shared task organizers which is used for this study. This study focused on offensive language identification on code-mixed low-resourced Dravidian language Tamil using four classifiers (Support Vector Machine, random forest, k-Nearest Neighbour and Naive Bayes) using χ 2 feature selection technique along with BoW and TF-IDF feature representation techniques using different combinations of n-grams. This proposed model achieved an accuracy of 76.96% while using linear SVM with TF-IDF feature representation technique. | OffTamil@DravideanLangTech-EACL2021: Offensive Language Identification in Tamil Text |
d253237047 | Mathematical reasoning skills are essential for general-purpose intelligent systems to perform tasks from grocery shopping to climate modeling.Towards evaluating and improving AI systems in this domain, we propose LĪLA, a unified mathematical reasoning benchmark consisting of 23 diverse tasks along four dimensions: (i) mathematical abilities e.g., arithmetic, calculus (ii) language format e.g., questionanswering, fill-in-the-blanks (iii) language diversity e.g., no language, simple language (iv) external knowledge e.g., commonsense, physics. We construct our benchmark by extending 20 datasets benchmark by collecting task instructions and solutions in the form of Python programs, thereby obtaining explainable solutions in addition to the correct answer. We additionally introduce two evaluation datasets to measure out-of-distribution performance and robustness to language perturbation. Finally, we introduce BHĀSKARA, a general-purpose mathematical reasoning model trained on LĪLA. Importantly, we find that multi-tasking leads to significant improvements (average relative improvement of 21.83% F1 score vs. single-task models), while the best performing model only obtains 60.40%, indicating the room for improvement in general mathematical reasoning and understanding. | LĪLA: A Unified Benchmark for Mathematical Reasoning |
d19035781 | Extending Translation Memories | |
d555889 | This demonstration presents an intelligent information platform MODEST. MODEST will provide enterprises with the services of retrieving news from websites, extracting commercial information, exploring customers' opinions, and analyzing collaborative/competitive social networks. In this way, enterprises can improve the competitive abilities and facilitate potential collaboration activities. At the meanwhile, MOD-EST can also help governments to acquire information about one single company or the entire board timely, and make prompt strategies for better support. Currently, MODEST is applied to the pillar industries of Hong Kong, including innovative finance, modem logistics, information technology, etc. | Web Information Mining and Decision Support Platform for the Modern Service Industry |
d12596803 | Sentiment Classification seeks to identify a piece of text according to its author's general feeling toward their subject, be it positive or negative. Traditional machine learning techniques have been applied to this problem with reasonable success, but they have been shown to work well only when there is a good match between the training and test data with respect to topic. This paper demonstrates that match with respect to domain and time is also important, and presents preliminary experiments with training data labeled with emoticons, which has the potential of being independent of domain, topic and time. | Using Emoticons to reduce Dependency in Machine Learning Techniques for Sentiment Classification |
d15908417 | In this paper, we discuss how statements about defaults and various forms of exceptions to them can be incorporated into an existing controlled natural language. We show how these defaults and exceptions are translated and represented in the answer set programming paradigm in order to support automated reasoning. | Working with Defaults in a Controlled Natural Language |
d11000512 | Last Words Are We Near the End of the Journal? | |
d10509498 | Most existing machine translation systems operate at the level of words, relying on explicit segmentation to extract tokens. We introduce a neural machine translation (NMT) model that maps a source character sequence to a target character sequence without any segmentation. We employ a character-level convolutional network with max-pooling at the encoder to reduce the length of source representation, allowing the model to be trained at a speed comparable to subword-level models while capturing local regularities. Our character-to-character model outperforms a recently proposed baseline with a subwordlevel encoder on WMT'15 DE-EN and CS-EN, and gives comparable performance on FI-EN and RU-EN. We then demonstrate that it is possible to share a single characterlevel encoder across multiple languages by training a model on a many-to-one translation task. In this multilingual setting, the character-level encoder significantly outperforms the subword-level encoder on all the language pairs. We observe that on CS-EN, FI-EN and RU-EN, the quality of the multilingual character-level translation even surpasses the models specifically trained on that language pair alone, both in terms of BLEU score and human judgment. | Fully Character-Level Neural Machine Translation without Explicit Segmentation |
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