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Automatic sentiment classification has been extensively studied and applied in recent years. However, sentiment is expressed dif- ferently in different domains, and annotating corpora for every possible domain of interest …...

When a word sense disambiguation (WSD) system is trained on one domain but ap- plied to a different domain, a drop in ac- curacy is frequently observed. This high- lights …...

We describe some challenges of adaptation in the 2007 CoNLL Shared Task on Domain Adaptation. Our error analysis for this task suggests that a primary source of error is differences …...

This paper reports a hybridization experi­ ment, where a baseline ML dependency pars­ er, LingPars, was allowed access to Con­ straint Grammar analyses provided by a rule­ based parser (EngGram) …...

Domain adaptation is an important problem in natural language processing (NLP) due to the lack of labeled data in novel domains. In this paper, we study the domain adaptation problem …...

Creating large amounts of annotated data to train statistical PCFG parsers is expensive, and the performance of such parsers declines when training and test data are taken from different domains. …...

tra Statistical machine translation systems are usually trained on large amounts of bilingual text and monolingual text. In this paper, we propose a method to per- form domain adaptation for …...

We present a novel hierarchical prior struc- ture for supervised transfer learning in named entity recognition, motivated by the common structure of feature spaces for this task across natural language …...

We describe the experiments of the UC Berke- ley team on improving English-Spanish ma- chine translation of news text, as part of the WMT’08 Shared Translation Task. We ex- periment …...

In this paper we explore robustness and domain adaptation issues for Word Sense Disambiguation (WSD) using Singular Value Decomposition (SVD) and unlabeled data. We focus on the semi-supervised do- main …...

We address corpus building situations, where complete annotations to the whole corpus is time consuming and unrealistic. Thus, annotation is done only on crucial part of sentences, or contains unresolved …...

The accuracy of current word sense disam- biguation (WSD) systems is affected by the fine-grained sense inventory of WordNet as well as a lack of training examples. Using the WSD …...

Domain adaptation is an important problem in named entity recognition (NER). NER classi- fiers usually lose accuracy in the domain trans- fer due to the different data distribution be- tween …...

Bootstrapping is the process of improving the performance of a trained classifier by iteratively adding data that is labeled by the classifier itself to the training set, and retraining the …...

In this paper we extend a shallow parser [6] with prepositional phrase attachment. Although the PP attachment task is a well-studied task in a discriminative learning context, it is mostly …...

Named entity recognition systems sometimes have difficulty when applied to data from do- mains that do not closely match the training data. We first use a simple rule-based tech- nique …...

Entity linking refers entity mentions in a document to their representations in a knowledge base (KB). In this paper, we propose to use additional information sources from Wikipedia to find …...

Previous research has demonstrated the importance of handling differences be- tween domains such as “newswire” and “biomedicine” when porting NLP systems from one domain to another. In this paper we …...

Problems stemming from domain adaptation continue to plague the statistical natural lan- guage processing community. There has been continuing work trying to find general purpose algorithms to alleviate this problem. …...

It is well known that parsing accuracy suf- fers when a model is applied to out-of-domain data. It is also known that the most benefi- cial data to parse a …...

This paper reports on the ongoing work fo- cused on domain adaptation of statistical machine translation using domain-specific data obtained by domain-focused web crawling. We present a strategy for crawl- …...

In this paper an unsupervised approach to do- main adaptation is presented, which exploits external knowledge sources in order to port a classification model into a new thematic do- main. …...

Domain adaptation (DA), which involves adapting a classifier developed from source to target data, has been studied intensively in re- cent years. However, when DA for word sense disambiguation (WSD) …...

We tackle the problem of domain adapta- tion of Statistical Machine Translation by exploiting domain-specific data acquired by domain-focused web-crawling. We de- sign and evaluate a procedure for auto- matic …...

AbstractDomain adaptation is an important task in order for NLP systems to work well in real applications. There has been extensive research on this topic. In this paper, we address …...

Impact of the level of supervision on Web-based language model domain adaptation Domain adaptation of a language model aims at re-estimating word sequence probabilities in order to better match the …...

In Statistical Machine Translation, in- domain and out-of-domain training data are not always clearly delineated. This paper investigates how we can still use mixture-modeling techniques for domain adaptation in such …...

We investigate the problem of domain adaptation for parallel data in Statistical Machine Translation (SMT). While tech- niques for domain adaptation of monolin- gual data can be borrowed for parallel …...

In recent years, statistical parsers have reached high performance levels on well-edited texts. Domain adaptation techniques have improved parsing results on text genres differing from the journalistic data most parsers …...

This paper describes the UPC participation in the WMT 12 evaluation campaign. All sys- tems presented are based on standard phrase- based Moses systems. Variations adopted sev- eral improvement techniques …...

Microblogging services such as Twitter have become popular media for real-time user- created news reporting. Such communica- tion often happens in parallel in different lan- guages, e.g., microblog posts related …...

AbstractDomain adaptation is an important topic for natural language processing. There has been extensive research on the topic and various methods have been explored, including training data selection, model combination, …...

This paper describes our submission for the *SEM shared task of Semantic Textual Sim- ilarity. We estimate the semantic similarity between two sentences using regression mod- els with features: 1) …...

Relation Extraction (RE) is the task of extracting semantic relationships between entities in text. Recent studies on rela- tion extraction are mostly supervised. The clear drawback of supervised methods is …...

Domain adaptation has been popularly stud- ied on exploiting labeled information from a source domain to learn a prediction model in a target domain. In this paper, we develop a …...

This paper describes the TALP participa- tion in the WMT13 evaluation campaign. Our participation is based on the combi- nation of several statistical machine trans- lation systems: based on standard …...

The rise of social media has brought compu- tational linguistics in ever-closer contact with bad language: text that defies our expecta- tions about vocabulary, spelling, and syntax. This paper surveys …...

Genre classification has been found to improve performance in many applications of statistical NLP, including language modeling for spoken language, domain adaptation of statistical parsers, and machine translation. It has …...

AbstractRecent years have seen increased interest in adapting translation models to test domains that are known in advance as well as using latent topic representations to adapt to unknown test …...

This paper presents the first experiments on identifying implicit discourse relations (i.e., relations lacking an overt discourse connective) in French. Given the little amount of annotated data for this task, …...

AbstractWe present FLORS, a new part-of-speech tagger for domain adaptation. FLORS uses robust representations that work especially well for unknown words and for known words with unseen tags. FLORS is …...

AbstractAnnotation of data is a time-consuming process, but necessary for many state-of-the-art solutions to NLP tasks, including semantic role labeling (SRL). In this paper, we show that language models may …...

. This paper presents the first experiments on French in automatic identification of implicit discourse relations (i.e. relations that lack an overt connective). Our systems exploit hand-labeled implicit examples, along …...

AbstractDespite many recent papers on Arabic Named Entity Recognition (NER) in the news domain, little work has been done on microblog NER. NER on microblogs presents many complications such as …...

We present a French to English transla- tion system for Wikipedia biography ar- ticles. We use training data from out- of-domain corpora and adapt the system for biographies. We propose …...

. In this article, we propose a study on the words or multi-words which are good indicators of the opinion polarity of a text but have different polarity depending on …...

. Statistical approaches used in machine translation (MT) require the availability of large parallel corpora for the task at hand. The relative scarcity of thes resources makes domain adaptation a …...

Domain adaptation for statistical machine translation is the task of altering general models to improve performance on the test domain. In this work, we suggest several novel weighting schemes based …...

Domain adaptation is a challenge for super- vised NLP systems because of expensive and time-consuming manual annotated re- sources. We present a novel method to adapt a supervised coreference resolution …...

Many discourse relations are explicitly marked with discourse connectives, and these examples could potentially serve as a plentiful source of training data for recog- nizing implicit discourse relations. How- ever, …...

We developed a system to extract Japanese anime-related words, i.e., Japanese NEs (named entities) in the anime-related do- main. Since the NEs in the area, such as the titles of …...

Risk assessment is a crucial activity for financial institutions because it helps them to determine the amount of capi- tal they should hold to assure their sta- bility. Flawed risk …...

We present novel models for domain adap- tation based on the neural network joint model (NNJM). Our models maximize the cross entropy by regularizing the loss function with respect to …...

In regard to document classification, semi- supervised learning using the Naive Bayes method and EM algorithm was a great suc- cess, and we refer to this method as NBEM in …...

Domain adaptation boosts translation quality on in-domain data, but translation quality for domain adapted systems on out-of-domain data tends to suffer. Users of web-based translation services expect high quality translation …...

Joint models have recently shown to improve the state-of-the-art in machine translation (MT). We apply EM-based mixture modeling and data selection techniques using two joint models, namely the Operation Sequence …...

AbstractWe present a novel fusion model for domain adaptation in Statistical Machine Translation. Our model is based on the joint source-target neural network Devlin et al., 2014, and is learned …...

AbstractExisting work on domain adaptation for statistical machine translation has consistently assumed access to a small sample from the test distribution (target domain) at training time. In practice, however, the …...

AbstractThis paper investigates self trained cross-show speaker diarization applied to collections of French TV archives, based on an i-vector/PLDA framework. The parameters used for i-vectors extraction and PLDA scoring are …...

We present the first domain adaptation model for authorship attribution to leverage unlabeled data. The model includes exten- sions to structural correspondence learning needed to make it appropriate for the …...

AbstractNeural network training has been shown to be advantageous in many natural language processing applications, such as language modelling or machine translation. In this paper, we describe in detail a …...

AbstractPopular techniques for domain adaptation such as the feature augmentation method of Daumé III (2009) have mostly been considered for sparse binary-valued features, but not for dense real-valued features such …...

AbstractDomain adaptation is a major challenge when applying machine translation to practical tasks. In this paper, we present domain adaptation methods for machine translation that assume multiple domains. The proposed …...

Domain adaptation is an important re- search topic in sentiment analysis area. Existing domain adaptation methods usu- ally transfer sentiment knowledge from only one source domain to target do- main. …...

AbstractThis paper describes the submission of the University of Edinburgh team to the IWSLT MT task for TED talks. We took part in four translation directions, en-de, de-en, en-fr, and …...

AbstractOut-of-vocabulary words (OOVs) are a ubiquitous and difficult problem in statistical machine translation (SMT). This paper studies different strategies of using BabelNet to alleviate the negative impact brought about by …...

This work discusses a mix of challenges aris- ing from Watson Discovery Advisor (WDA), an industrial strength descendant of the Wat- son Jeopardy! Question Answering system currently used in production …...

AbstractDomain adaptation is an important technology to handle domain dependence problem in sentiment analysis field. Existing methods usually rely on sentiment classifiers trained in source domains. However, their performance may …...

AbstractTwo types of data shift common in practice are 1. transferring from synthetic data to live user data (a deployment shift), and 2. transferring from stale data to current data …...

AbstractAn important problem in domain adaptation is to quickly generalize to a new domain with limited supervision given K existing domains. One approach is to retrain a global model across …...

AbstractWe introduce a neural network model that marries together ideas from two prominent strands of research on domain adaptation through representation learning: structural correspondence learning (SCL, (Blitzer et al., 2006)) …...

AbstractThis study proposes a system to participate in the Clinical TempEval 2017 shared task, a part of the SemEval 2017 Tasks. Domain adaptation was the main challenge this year. We …...

We describe the University of Mary- land machine translation systems submit- ted to the WMT17 German-English Ban- dit Learning Task. The task is to adapt a translation system to a …...

AbstractNeural machine translation (NMT) is a deep learning based approach for machine translation, which yields the state-of-the-art translation performance in scenarios where large-scale parallel corpora are available. Although the high-quality …...

AbstractDomain Adaptation arises when we aim at learning from source domain a model that can perform acceptably well on a different target domain. It is especially crucial for Natural Language …...

AbstractPreviously, neural methods in grammatical error correction (GEC) did not reach state-of-the-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural …...

Aspect identification in user generated texts by supervised text classification might suffer degradation in performance when changing to other domains than the one used for training. For referring to aspects …...

We propose a new method for domain adapta- tion by using a combination of multiple em- beddings for sentiment analysis. We first make the following embeddings for the doc- ument: …...

The paper introduces a multi-level annotation of the R. GVEDA, a fundamental Sanskrit text composed in the 2. millenium BCE that is important for South-Asian and Indo-European linguistics, as well …...

AbstractDomain adaptation for sentiment analysis is challenging due to the fact that supervised classifiers are very sensitive to changes in domain. The two most prominent approaches to this problem are …...

AbstractWe implemented the sentiment system in all five subtasks for English and Spanish. All subtasks involve emotion or sentiment intensity prediction (regression and ordinal classification) and emotions determining (multi-labels classification). …...

AbstractWe propose a new domain adaptation method for Combinatory Categorial Grammar (CCG) parsing, based on the idea of automatic generation of CCG corpora exploiting cheaper resources of dependency trees. Our …...

AbstractDue to limitation of labeled resources, cross-domain named entity recognition (NER) has been a challenging task. Most existing work considers a supervised setting, making use of labeled data for both …...

AbstractWe introduce a curriculum learning approach to adapt generic neural machine translation models to a specific domain. Samples are grouped by their similarities to the domain of interest and each …...

AbstractSpoken Language Understanding (SLU) converts user utterances into structured semantic representations. Data sparsity is one of the main obstacles of SLU due to the high cost of human annotation, especially …...

AbstractPerson-job fit has been an important task which aims to automatically match job positions with suitable candidates. Previous methods mainly focus on solving the match task in single-domain setting, which …...

This paper proposes a novel multilin- gual multistage fine-tuning approach for low-resource neural machine translation (NMT), taking a challenging Japanese– Russian pair for benchmarking. Al- though there are many solutions …...

AbstractIn domain adaptation for neural machine translation, translation performance can benefit from separating features into domain-specific features and common features. In this paper, we propose a method to explicitly model …...

AbstractContinued training is an effective method for domain adaptation in neural machine translation. However, in-domain gains from adaptation come at the expense of general-domain performance. In this work, we interpret …...

AbstractPivot Based Language Modeling (PBLM) (Ziser and Reichart, 2018a), combining LSTMs with pivot-based methods, has yielded significant progress in unsupervised domain adaptation. However, this approach is still challenged by the …...

AbstractDomain adaptation is an old and vexing problem for machine translation systems. The most common approach and successful to supervised adaptation is to fine-tune a baseline system with in-domain parallel …...

AbstractIn this paper, we deal with two problems in Japanese-English machine translation of news articles. The first problem is the quality of parallel corpora. Neural machine translation (NMT) systems suffer …...

AbstractBiomedical event extraction is a crucial task in order to automatically extract information from the increasingly growing body of biomedical literature. Despite advances in the methods in recent years, most …...

AbstractIn this paper, we deal with two problems in Japanese-English machine translation of news articles. The first problem is the quality of parallel corpora. Neural machine translation (NMT) systems suffer …...

AbstractPrior work has determined domain similarity using text-based features of a corpus. However, when using pre-trained word embeddings, the underlying text corpus might not be accessible anymore. Therefore, we propose …...

AbstractWe propose an end-to-end approach for synthetic QA data generation. Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate both answers and questions. In a …...

AbstractDomain adaptation of Pretrained Language Models (PTLMs) is typically achieved by unsupervised pretraining on target-domain text. While successful, this approach is expensive in terms of hardware, runtime and CO 2 …...

AbstractPre-training sentence encoders is effective in many natural language processing tasks including machine translation (MT) quality estimation (QE), due partly to the scarcity of annotated QE data required for supervised …...

AbstractNeural machine translation (NMT) models usually suffer from catastrophic forgetting during continual training where the models tend to gradually forget previously learned knowledge and swing to fit the newly added …...

AbstractThe MICE project (2018-2020) will deliver a middleware layer for improving the output quality of the eTranslation system of EC’s Connecting Europe Facility through additional services, such as domain adaptation …...

AbstractPivot-based neural representation models have led to significant progress in domain adaptation for NLP. However, previous research following this approach utilize only labeled data from the source domain and unlabeled …...

AbstractThis paper describes the participation of the NLP research team of the IPN Computer Research center in the WMT 2020 Similar Language Translation Task. We have submitted systems for the …...

AbstractNeural network methods exhibit strong performance only in a few resource-rich domains. Practitioners therefore employ domain adaptation from resource-rich domains that are, in most cases, distant from the target domain. …...

AbstractDomain adaptation assumes that samples from source and target domains are freely accessible during a training phase. However, such assumption is rarely plausible in the real-world and may causes data-privacy …...

AbstractAs a fine-grained task, the annotation cost of aspect term extraction is extremely high. Recent attempts alleviate this issue using domain adaptation that transfers common knowledge across domains. Since most …...

AbstractThe principle of independent causal mechanisms (ICM) states that generative processes of real world data consist of independent modules which do not influence or inform each other. While this idea …...

Supervised learning methods have proven to be effective for Aspect-Based Sentiment Anal- ysis (ABSA). However, the lack of fine- grained labeled data hinders their effective- ness in many domains. To …...

AbstractCrowdsourcing is regarded as one prospective solution for effective supervised learning, aiming to build large-scale annotated training data by crowd workers. Previous studies focus on reducing the influences from the …...

AbstractThis paper describes Huawei Artificial Intelligence Application Research Center’s neural machine translation systems and submissions to the WMT21 biomedical translation shared task. Four of the submissions achieve state-of-the-art BLEU scores …...

AbstractHow to effectively adapt neural machine translation (NMT) models according to emerging cases without retraining? Despite the great success of neural machine translation, updating the deployed models online remains a …...

AbstractRecent improvements in the predictive quality of natural language processing systems are often dependent on a substantial increase in the number of model parameters. This has led to various attempts …...

AbstractDomain Adaptation is widely used in practical applications of neural machine translation, which aims to achieve good performance on both general domain and in-domain data. However, the existing methods for …...

AbstractThe prevalence of the COVID-19 pandemic in day-to-day life has yielded large amounts of stance detection data on social media sites, as users turn to social media to share their …...

AbstractThe paradigm of pre-training followed by finetuning has become a standard procedure for NLP tasks, with a known problem of domain shift between the pre-training and downstream corpus. Previous works …...

AbstractOne of the difficulties in training dialogue systems is the lack of training data. We explore the possibility of creating dialogue data through the interaction between a dialogue system and …...

AbstractMany works proposed methods to improve the performance of Neural Machine Translation (NMT) models in a domain/multi-domain adaptation scenario. However, an understanding of how NMT baselines represent text domain information …...

Due to the multi-dimensional variation of tex- tual data, detection of event triggers from new domains can become a lot more challenging. This prompts a need to research on domain …...

AbstractThe increasing popularity of voice-based personal assistants provides new opportunities for conversational recommendation. One particularly interesting area is movie recommendation, which can benefit from an open-ended interaction with the user, …...

AbstractRecently, fine-tuning the pre-trained language model (PrLM) on labeled sentiment datasets demonstrates impressive performance. However, collecting labeled sentiment dataset is time-consuming, and fine-tuning the whole PrLM brings about much computation …...

AbstractThis paper considers continual learning of large-scale pretrained neural machine translation model without accessing the previous training data or introducing model separation. We argue that the widely used regularization-based methods, …...

AbstractNatural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples. In this paper we propose a controllable generation approach in order to …...

AbstractQuestion answering (QA) has demonstrated impressive progress in answering questions from customized domains. Nevertheless, domain adaptation remains one of the most elusive challenges for QA systems, especially when QA systems …...

AbstractComplex word identification (CWI) is a cornerstone process towards proper text simplification. CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which …...

AbstractState-of-the-art approaches for hate-speech detection usually exhibit poor performance in out-of-domain settings. This occurs, typically, due to classifiers overemphasizing source-specific information that negatively impacts its domain invariance. Prior work has …...

AbstractExisting approaches to mitigate demographic biases evaluate on monolingual data, however, multilingual data has not been examined. In this work, we treat the gender as domains (e.g., male vs. female) …...

AbstractThe remarkable success of large language models has been driven by dense models trained on massive unlabeled, unstructured corpora. These corpora typically contain text from diverse, heterogeneous sources, but information …...

AbstractThis paper presents text mining approaches on German-speaking job advertisements to enable social science research on the development of the labour market over the last 30 years. In order to …...

AbstractDense retrieval approaches can overcome the lexical gap and lead to significantly improved search results. However, they require large amounts of training data which is not available for most domains. …...

AbstractDomain adaptation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as bioNER, domain adaptation methods often suffer from the challenging linguistic …...

AbstractDictionary-based data augmentation techniques have been used in the field of domain adaptation to learn words that do not appear in the parallel training data of a machine translation model. …...

AbstractBack-translation has been proven to be effective in unsupervised domain adaptation of neural machine translation (NMT). However, the existing back-translation methods mainly improve domain adaptability by generating in-domain pseudo-parallel data …...

AbstractRecent explorations of large-scale pre-trained language models (PLMs) have revealed the power of PLMs with huge amounts of parameters, setting off a wave of training ever-larger PLMs. However, it requires …...

AbstractOff-the-shelf models are widely used by computational social science researchers to measure properties of text, such as sentiment. However, without access to source data it is difficult to account for …...

AbstractAdversarial training, which minimizes the loss of adversarially perturbed examples, has received considerable attention. However, these methods require modifying all model parameters and optimizing the model from scratch, which is …...

AbstractSynthesizing QA pairs with a question generator (QG) on the target domain has become a popular approach for domain adaptation of question answering (QA) models. Since synthetic questions are often …...

AbstractDiscourse analysis allows us to attain inferences of a text document that extend beyond the sentence-level. The current performance of discourse models is very low on texts outside of the …...

AbstractIR models using a pretrained language model significantly outperform lexical approaches like BM25. In particular, SPLADE, which encodes texts to sparse vectors, is an effective model for practical use because …...

AbstractDomain-specific documents cover terminologies and specialized knowledge. This has been the main challenge of domain-specific document retrieval systems. Previous approaches propose domain-adaptation and transfer learning methods to alleviate this problem. …...

Pretraining models such as BERT, have achieved high accuracy in various natu- ral language processing tasks by pretrain- ing on a large corpus and fine-tuning on downstream task data. However, …...

AbstractCross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain. Since labeled data only exists in …...

AbstractSentence embeddings enable us to capture the semantic similarity of short texts. Most sentence embedding models are trained for general semantic textual similarity tasks. Therefore, to use sentence embeddings in …...

AbstractConflict prediction in communication is integral to the design of virtual agents that support successful teamwork by providing timely assistance. The aim of our research is to analyze discourse to …...

AbstractSemantic parsing plays a key role in digital voice assistants such as Alexa, Siri, and Google Assistant by mapping natural language to structured meaning representations. When we want to improve …...

AbstractWe propose a novel approach to learn domain-specific plausible materials for components in the vehicle repair domain by probing Pretrained Language Models (PLMs) in a cloze task style setting to …...

AbstractWe present the system proposed by the MilaNLP team for the Explainable Detection of Online Sexism (EDOS) shared task. We propose an ensemble modeling approach to combine different classifiers trained …...

AbstractDomain adaptation allows generative language models to address specific flaws caused by the domain shift of their application. However, the traditional adaptation by further training on in-domain data rapidly weakens …...

AbstractWhile quality estimation (QE) can play an important role in the translation process, its effectiveness relies on the availability and quality of training data. For QE in particular, high-quality labeled …...

AbstractNamed Entity Recognition is a key Natural Language Processing task whose performance is sensitive to choice of genre and language. A unified NER model across multiple genres and languages is …...

AbstractHealth-related speech datasets are often small and varied in focus. This makes it difficult to leverage them to effectively support healthcare goals. Robust transfer of linguistic features across different datasets …...

AbstractIn this paper, we cast Legal Judgment Prediction on European Court of Human Rights cases into an article-aware classification task, where the case outcome is classified from a combined input …...