[PDF] Top 20 Portuguese text generation using factored language models

... clear comparison among the participant systems, a number of input-specification issues remain to be solved. For a discus- sion on these difficulties and future improvements, see [13]. Out of the five participants in the ... See full document

... subtasks using n-gram and factored language models alike: the ex- periments in (Novais et ...n-grams models to address the issues of Portuguese NP and VP lexical choice, ordering ... See full document

... The approach of joint learning has been tested in the literature in other domains than NLG/NLU for tasks such machine translation (Cheng et al., 2016; He et al., 2016; Tu et al., 2017) and speech processing (Tjandra et ... See full document

... Traditionally, text generation models are trained by going over a gold sequence of symbols (char- acters or words) from left-to-right, and maximiz- ing the probability of the next symbol given the ... See full document

... side generation is one of the main issues, when various ill-formed or fragmented structures come out after ...generate text fragments instead of full sentences, in order to increase the ... See full document

... most text generation AAC devices typically already rely upon symbol, word and phrase prediction from statistical language models to speed text input, the predictions of the conversation ... See full document

... Mapping an application semantics to surface strings usually involves the use of surface realiza- tion grammars or similar resources, which can be either built manually (e.g., Bateman, 1997) or ac- quired automatically ... See full document

... Several approaches need alignments to learn rules and ways to linearise and compress data in AMR graphs. This is a problem because there is a need to manually align AMR graphs and target sentences in order to allow the ... See full document

... During inference, each word is generated in series based on formerly generated phrases, while for the duration of education floor-reality words are used for every time step. Recently, adversarial training has emerged as ... See full document

... FLMs can be trained easily by estimating conditional probabilities from feature counts over a corpus, and they offer efficient decoding techniques for real-time generation. However, FLMs do not scale well to large ... See full document

... statistical language modelling is n- grams model. The n-grams language model are based on statistical of how likely words are to follow each ...In language modelling, the system wants to compute ... See full document

... robust text geolocation and scales well to large training ...topic models or Bayesian methods would likely provide more insight with regard to the most dis- criminative and geolocatable ...trees ... See full document

... that using sim- ulation runs will improve system performance to a level such that the first collection of real user data will contain a reasonable rate of task success, ul- timately providing a more useful ... See full document

... AUTOMATIC TEXT GENERATION 1 0 INTRODUCTION Automatic text generation is the generation of natural language texts by computer It has applications in automatic documentation systems, automatic letter wr[.] ... See full document

... FAIRSEQ includes features designed to improve re- producibility and forward compatibility. For ex- ample, checkpoints contain the full state of the model, optimizer and dataloader, so that results are reproducible if ... See full document

... method using the character sequence as opposed to words as the nexus for kernel creation, and showed promising results for discrimina- tion between texts of different languages and for clustering based on string ... See full document

... the language of English there are only 26 letters and each of them have a different ...lengthy text has to be read because it will be hard to understand the words that is being read character by ... See full document

... unigram language models, extensions can be made to more complex models ...topic models, etc.) and to longer n-gram contexts. In the case of topic models, the model will be related to ... See full document

... system using morpho-syntactic and semantic information where the target case markers are generated based on the suffixes and semantic relations of the source sen- ...developed using Bengali script based ... See full document

... into factored RNNLM, such as context-free rule productions, constituent/head features, and head-to-head dependencies that can be extracted using parser ...of factored RNNLM using graphical ... See full document