N-граммалық Тіл Моделі

We present an algorithm to adapt a n-gram language model to a document as it is dictated. The observed partial document is used to esti- mate a unigram distribution for …...

The conventional n-gram language model exploits only the immediate context of historical words without exploring long-distance semantic information. In this paper, we present a new information source extracted from latent …...

This paper presents a discriminative pruning method of n-gram language model for Chinese word segmentation. To reduce the size of the language model that is used in a Chinese word …...

In this paper, we argue that n-gram lan- guage models are not sufficient to address word reordering required for Machine Trans- lation. We propose a new distortion model that can …...

In this paper we describe and evaluate several statistical models for the task of realization ranking, i.e. the problem of discriminating between competing surface realizations generated for a given input …...

AbstractIn syntax-directed translation, the source-language input is first parsed into a parse-tree, which is then recursively converted into a string in the target-language. We model this conversion by an extended …...

In this paper, we propose a novel discrim- inative language model, which can be ap- plied quite generally. Compared to the well known N-gram language models, dis- criminative language models …...

We present an efficient, novel two-pass approach to mitigate the computational impact resulting from online intersection of an n-gram language model (LM) and a probabilistic synchronous context-free grammar (PSCFG) for …...

AbstractIn this paper, we describe the system and approach used by Institute for Infocomm Research (I2R) for the IWSLT 2007 spoken language evaluation campaign. A multi-pass approach is exploited to …...

User logs of search engines have recently been applied successfully to improve var- ious aspects of web search quality. In this paper, we will apply pairs of user queries and …...

We have designed, implemented and eval- uated an end-to-end system spellcheck- ing and autocorrection system that does not require any manually annotated train- ing data. The World Wide Web is …...

A machine translated sentence is seldom completely correct. Confidence measures are designed to detect incorrect words, phrases or sentences, or to provide an esti- mation of the probability of correctness. …...

N-gram language models are a major resource bottleneck in machine translation. In this pa- per, we present several language model imple- mentations that are both highly compact and fast to …...

Cross-language information retrieval today is dominated by techniques that rely principally on context-independent token-to-token mappings despite the fact that state-of-the-art statistical machine translation systems now have far richer translation models …...

A fundamental problem in text generation is word ordering. Word ordering is a com- putationally difficult problem, which can be constrained to some extent for particu- lar applications, for example …...

In this paper we address the problem of solving substitution ciphers using a beam search approach. We present a concep- tually consistent and easy to implement method that improves the …...

This paper integrates techniques in natural language processing and computer vision to improve recognition and description of entities and activities in real-world videos. We propose a strategy for generating textual …...

AbstractThe role of language models in SMT is to promote fluent translation output, but traditional n-gram language models are unable to capture fluency phenomena between distant words, such as some …...

For many applications, the query speed of N-gram language models is a computa- tional bottleneck. Although massively par- allel hardware like GPUs offer a poten- tial solution to this bottleneck, …...

Recent work on word ordering has argued that syntactic structure is important, or even re- quired, for effectively recovering the order of a sentence. We find that, in fact, an …...

Previous phrase-based approaches to Au- tomatic Post-editing (APE) have shown that the dependency of MT errors from the source sentence can be exploited by jointly learning from source and target …...

AbstractWe propose algorithms to train production-quality n-gram language models using federated learning. Federated learning is a distributed computation platform that can be used to train global models for portable devices …...

This paper is devoted to developing a re- current neural network (RNN) solution for segmenting the unpunctuated transcripts generated by automatic speech recognition for simultaneous interpretation. RNNs are effective in …...

AbstractContext-aware historical text normalisation is a severely under-researched area. To fill the gap we propose a context-aware normalisation approach that relies on the state-of-the-art methods in neural machine translation and …...