[PDF] Top 20 A General Technique to Train Language Models on Language Models
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A General Technique to Train Language Models on Language Models
... A third approach is to construct a training corpus from the PCFG by means of a (pseudo)random generator of sentences, such that sentences that are more likely according to the PCFG are generated with greater likelihood. ... See full document
14
Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management
... to train image recognition models and the vocabulary size to train language models, our pro- posed curriculum data is based on the number of slots of user goals to solve goal oriented ... See full document
6
Language Models for Machine Translation: Original vs Translated Texts
... to train n-gram language models from various ...limit language models to a fixed vocabulary and map out-of-vocabulary (OOV) tokens to a unique symbol to better control the OOV rates ... See full document
28
The Helsinki Neural Machine Translation System
... SMT models using KenLM for language modeling (Heafield, 2011) and BLEU-based MERT for tun- ...alignment models with a Bayesian extension and Gibbs ... See full document
10
Temporal Analysis of Language through Neural Language Models
... in language is of in- terest to theoretical linguists as well as NLP re- searchers working with diachronic ...we train a Neural Language Model (NLM) on yearly corpora to obtain word vectors for each ... See full document
5
Half Context Language Models
... hierarchical language models (and a back-off variety [Zitouni 2007]) where each vocabulary item constitutes a leaf node in a word-tree, words are clustered into classes, and, in a recursive process, classes ... See full document
23
Proceedings of the 5th Workshop on Cognitive Aspects of Computational Language Learning (CogACLL)
... natural language processing ...and language processing tasks, including ...human language acquisition and ...computational models attempt to study language tasks under cognitively ... See full document
10
Reference Aware Language Models
... For models other than Ta- ble Pointer, because the tokens never appear in the training set, the perplexity is quite high, while Ta- ble Pointer can predict these tokens much more ... See full document
10
Shrinking Exponential Language Models
... of language models belonging to the ex- ponential family, the test set cross-entropy of a model can be accurately predicted from its training set cross-entropy and its parameter ...a language model ... See full document
9
UParse: the Edinburgh system for the CoNLL 2017 UD shared task
... Next, we describe UParse, the extended version of DENSE which we use for the UD shared task. As mentioned in Section 1, UParse is a combination of monolingual, multilingual, UDPipe baseline, and delexicalized ... See full document
11
Proceedings of the Sixth Workshop on Cognitive Aspects of Computational Language Learning
... natural language processing ...and language processing tasks, including ...human language acquisition and ...computational models attempt to study language tasks under cognitively ... See full document
12
A Posteriori Individual Word Language Models for Vietnamese Language
... Previously, for similar experiments, Sicilia-Garcia et al. (2005) reported improvements of 68%-69% for 7-gram models. Nevertheless, that was based on the flawed calculation previously described in the section, A ... See full document
24
Language Models as Knowledge Bases?
... of language models as potential representations of relational knowledge, we are interested in the relational knowledge al- ready present in pretrained o ff -the-shelf language models such as ... See full document
11
Discourse Models and Language Comprehension
... uhere an utterance s meaning may depend upon who the speaker is,.. are, what the Durpose of the conversation is, and so o n.[r] ... See full document
17
Learning to Create and Reuse Words in Open Vocabulary Neural Language Modeling
... To evaluate our model, we perform ablation ex- periments with variants of our model without the cache or hierarchical structure. In addition to stan- dard English data sets (PTB and WikiText-2), we introduce a new ... See full document
11
PAWS X: A Cross lingual Adversarial Dataset for Paraphrase Identification
... Most existing work on adversarial data gen- eration focuses on English. For example, PAWS (Paraphrase Adversaries from Word Scrambling) (Zhang et al., 2019) consists of challenging English paraphrase identification pairs ... See full document
6
Paraphrasing with Large Language Models
... Paraphrase generation has attracted a number of different NLP approaches. These have included rule-based approaches (McKeown, 1979; Meteer and Shaked, 1988) and data-driven methods (Mad- nani and Dorr, 2010), with ... See full document
6
Suffix Trees as Language Models
... The implementation of the STLM relies on a compressed version of a suffix tree, which is enriched by suffix links. Suffix links are edges that are added incrementally at build- ing time to each node and that connect a ... See full document
8
Towards Quantum Language Models
... considered language has to be small in order to keep the model computationally ...the train- ing of this model is rather slow and requires rele- vant computational resources even for small prob- ... See full document
10
Simple task specific bilingual word embeddings
... We introduce a simple wrapper method that uses off-the-shelf word embedding algorithms to learn task-specific bilingual word em- beddings. We use a small dictionary of easily-obtainable task-specific word equiva- lence ... See full document
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