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Lexical decay is the phenomenon underlying the dating tech- niques known as "glottochronology" and"lexicostatistics." Much of the contraversial nature of work in this field is the result of extremely imprecise …...

Speech recognition systems incorporate a language model which, at each stage of the recognition task, assigns a probabil- ity of occurrence to each word in the vocabulary. A class of …...

This paper presents a computer-tool tea- ching system supplied by a language proce- ssor. Its aim is to correct mistakes in texts written by foreign students learning Russian as a …...

The Lincoln stress-resistant HMM CSR has been extended to large vocabulary continuous speech for both speaker-dependent (SD) and speaker-independent (SI) tasks. Performance on the DARPA Resource Manage- ment task (991 …...

This paper describes an algorithm for performing rapid match on continuous speech that makes it possible to rec- ognize sentences from an 842 word vocabulary on a desk- top 33 …...

We have developed an algorithm for the automatic conversion of dictated English sentences to written text, with essentially no restriction on the nature of the mate- rial dictated. We require …...

Phonetic baseforms are the basic recognition units in most large vocabulary speech recognition systems. These base- forms are usually determined by hand once a vocabulary is chosen and not modified …...

SRI and U.C. Berkeley have begun a cooper- ative effort to develop a new architecture for real-time implementation of spoken language systems (SLS). Our goal is to develop fast speech …...

The Lincoln robust HMM recognizer has been con- verted from a single Ganssian or Gaussian mixture pdf per state to tied mixtures in which a single set of Gaus- sians …...

AbstractULTRA (Universal Language TRAnslator) is a multilingual, interlingual machine translation system currently under development at the Computing Research Laboratory at New Mexico State University. It translates between five languages (Chinese, …...

A phonetic representation of a language is used to describe the corresponding pronunciation and synthesize the acoustic model of any vocabulary. In order to obtain better phonetic representation, context-dependent units …...

In this paper we explore a learning-based ap- proach to the problem of predicting language impairment in children. We analyzed sponta- neous narratives of children and extracted fea- tures measuring …...

We present a novel strategy to derive new translation units using an additional bilin- gual corpus and a previously trained SMT system. The units were used to adapt the SMT …...

Most previous research on automated speech scoring has focused on restricted, predictable speech. For automated scoring of unrestricted spontaneous speech, speech proficiency has been evaluated primarily on aspects of pro- …...

While there have been many studies on measuring the size of learners’ vocabulary or the vocabulary they should learn, there have been few studies on what kind of words learners …...

WoNeF, an improved, extended and evaluated automatic French translation of WordNet Identifying the various possible meanings of each word of the vocabulary is a difficult problem that requires a lot …...

Despite the overwhelming use of statistical language models in speech recognition, machine translation, and several other domains, few high probability guarantees exist on their generaliza- tion error. In this paper, …...

We present a novel approach for analysing and classifying lyrics, experimenting both with n- gram models and more sophisticated features that model different dimensions of a song text, such as …...

AbstractKeyphrase annotation is the task of identifying textual units that represent the main content of a document. Keyphrase annotation is either carried out by extracting the most important phrases from …...

AbstractThis paper presents a model for Arabic morphological disambiguation based on Recurrent Neural Networks (RNN). We train Long Short-Term Memory (LSTM) cells in several configurations and embedding levels to model …...

AbstractSentence compression condenses a sentence while preserving its most important contents. Delete-based models have the strong ability to delete undesired words, while generate-based models are able to reorder or rephrase …...

AbstractLexicon based methods for sentiment analysis rely on high quality polarity lexicons. In recent years, automatic methods for inducing lexicons have increased the viability of lexicon based methods for polarity …...

AbstractAutomatic image description systems typically produce generic sentences that only make use of a small subset of the vocabulary available to them. In this paper, we consider the production of …...

AbstractSelf-normalizing discriminative models approximate the normalized probability of a class without having to compute the partition function. In the context of language modeling, this property is particularly appealing as it …...

Named Entity Recognition (NER) is an essential task in Natural Language Processing. Memory Enhanced CRF (MECRF) integrates external memory to extend Conditional Random Field (CRF) to capture long-range dependencies with …...

AbstractWe focus on the problem of language modeling for code-switched language, in the context of automatic speech recognition (ASR). Language modeling for code-switched language is challenging for (at least) three …...

AbstractWe introduce the first attempt at automatic speech recognition (ASR) in Inuktitut, as a representative for polysynthetic, low-resource languages, like many of the 900 Indigenous languages spoken in the Americas. …...

AbstractDue to the compelling improvements brought by BERT, many recent representation models adopted the Transformer architecture as their main building block, consequently inheriting the wordpiece tokenization system despite it not …...

AbstractGiven the well-established usefulness of part-of-speech tag annotations in many syntactically oriented downstream NLP tasks, the recently proposed notion of semantic tagging (Bjerva et al. 2016) aims at tagging words …...

AbstractTransfer learning is a popular strategy to improve the quality of low-resource machine translation. For an optimal transfer of the embedding layer, the child and parent model should share a …...

AbstractMost of the unsupervised dependency parsers are based on first-order probabilistic generative models that only consider local parent-child information. Inspired by second-order supervised dependency parsing, we proposed a second-order extension …...

AbstractResearch on the application of NLP in symbol-based Augmentative and Alternative Communication (AAC) tools for improving social interaction support is scarce. We contribute a novel method for generating context-related vocabulary …...

AbstractA well-known limitation in pretrain-finetune paradigm lies in its inflexibility caused by the one-size-fits-all vocabulary. This potentially weakens the effect when applying pretrained models into natural language generation (NLG) tasks, …...

AbstractIn this work, we provide a systematic and comprehensive empirical comparison of pretrained multilingual language models versus their monolingual counterparts with regard to their monolingual task performance. We study a …...

AbstractThe choice of token vocabulary affects the performance of machine translation. This paper aims to figure out what is a good vocabulary and whether we can find the optimal vocabulary …...

This paper presents WORDGUESS, a game- with-a-purpose vocabulary training where –in order to guess a target word (such as snow)– the player is offered associations of that tar- get word …...

AbstractWhen interested in an internal web ap-plication for MT, corporate customers always ask how reliable terminology will be in their translations. Coherent vocabulary is crucial in many aspects of corporate …...

AbstractWith the necessity of privacy protection, it becomes increasingly vital to train deep neural models in a federated learning manner for natural language processing (NLP) tasks. However, recent studies show …...

AbstractApplying Reinforcement learning (RL) following maximum likelihood estimation (MLE) pre-training is a versatile method for enhancing neural machine translation (NMT) performance. However, recent work has argued that the gains produced …...

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 …...

AbstractNamed Entity Recognition (NER) is a fundamental task in information extraction that locates the mentions of named entities and classifies them in unstructured texts. Previous studies typically used hidden Markov …...

AbstractResearchers have traditionally recruited native speakers to provide annotations for the widely used benchmark datasets. But there are languages for which recruiting native speakers is difficult, and it would help …...