[PDF] Top 20 Intelligent Selection of Language Model Training Data
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Intelligent Selection of Language Model Training Data
... difference selection method in- troduced here seems to produce language mod- els that are both a better match to texts in a re- stricted domain, and require less data for train- ing, than any of the ... See full document
5
Data point selection for self training
... sparse data problems for statis- tical ...self- training is a cheap and effective method for improving parsing accuracy for morphologi- cally rich ... See full document
6
Language Model Based Grammatical Error Correction without Annotated Training Data
... The core idea behind language modelling in GEC is that low probability sequences are more likely to contain grammatical errors than high probabil- ity sequences. For example, *discuss about the problem is expected ... See full document
7
Improving Language Model Adaptation using Automatic Data Selection and Neural Network
... Since language model (LM) is very sensitive to domain mismatch between training and test data, using a group of techniques to adapt a big LM to specific domains is quite ...automatic ... See full document
7
On Using Written Language Training Data for Spoken Language Modeling
... On Using Written Language Training Data for Spoken Language Modeling On Using Written Language Training Data for Spoken Language Modeling R Schwartz, L Nguyen, F Kubala, G Chou, G Zavaliagkos t, J Mak[.] ... See full document
5
NAIST at the NLI 2013 Shared Task
... feature selection using native language frequency for the closed track and Capping and the Sampling data to balance the size of training data for the open ...feature selection ... See full document
6
Classification-based spoken text selection for LVCSR language modeling
... VoiceTra4U-M data (VT-DEV) to tune obtain an interpolation ...media data (ALL), which contains both “written” and “spoken” utterances, the average WER improvement of the proposed CRF, LSTM, and SVM became ... See full document
12
The Helsinki Neural Machine Translation System
... tical MT. Both techniques are popular in neural MT but their impact on statistical MT has not been evaluated properly before. Therefore, we started a systematic comparison of different se- tups including various types of ... See full document
10
Reinforced Training Data Selection for Domain Adaptation
... different data and ...of) training instance at each step, where previous decision should influence later ...a training sample and the tar- get domain, and to guide the selection process with ... See full document
12
Noisy SMS Machine Translation in Low Density Languages
... the data, we see there is substantially more punctuation in the clean set than in the ...ken language translation, where punctuation predic- tion on the source language prior to translation has been ... See full document
7
Training Connectionist Models for the Structured Language Model
... SLM components. It is worth noting that the ap- proximated Kneser-Ney smoothed models could not improve the PPL after one iteration of EM training. One possible reason is that in order to apply Kneser- Ney ... See full document
8
Machine Translation with parfda , Moses, kenlm nplm , and PRO
... select training and language model instances to build Moses (Koehn et ...monolingual data available for building SMT ...English-Turkish language pair (Bic¸ici, 2018) when generating the ... See full document
7
Non Native Users in the Let’s Go!! Spoken Dialogue System: Dealing with Linguistic Mismatch
... Non-Native Data using a Native and a Mixed Language Model and non-native utterances ...acoustic model matching all non-native ...non-native data improves perfor- mance on native speech ... See full document
8
Improve Neural Entity Recognition via Multi Task Data Selection and Constrained Decoding
... Multi-task data selection removes noise from training data, while constrained decoding further improves the model by exploiting global and external information ...adaptive data ... See full document
6
EMNLP versus ACL: Analyzing NLP research over time
... Parsing Model, Dependency Trees 12 Words, Other Hand, Natural Language, Other Words, Corpus, Language Processing, Model, Information, TestSet, Language 13 Pos Tagging, Pos Tags, Word ... See full document
5
Feature Rich Error Detection in Scientific Writing Using Logistic Regression
... the training data, the system is immediately bi- ased towards high probabilities for true labels, trying to compensate the superior number of false labels in the training ...feature selection ... See full document
10
Adapting to Learner Errors with Minimal Supervision
... learner data raises the question of the trade-off between the useful information provided in the form of expensive supervision and the robustness obtained from training on large amounts of native ...modes: ... See full document
38
Judicious Selection of Training Data in Assisting Language for Multilingual Neural NER
... remaining language datasets did not have official train and development splits ...for training the model and remaining as development ...assisting language sentences and oversample primary ... See full document
6
Effective Selection of Translation Model Training Data
... the data selection. The in- domain data is collected from CWMT09, which consists of spoken dialogues in a travel setting, containing approximately 50,000 parallel sen- tence pairs in English and ... See full document
5
Prompsit’s submission to WMT 2018 Parallel Corpus Filtering shared task
... Prompsit Language Engi- neering’s submissions to the WMT 2018 paral- lel corpus filtering shared ...and language model per- plexity combined with n-gram saturation (aimed at achieving fluency and ... See full document
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