There are many Language Models for the English language according to its worldwide relevance. However, for the Spanish language, even if it is a widely spoken language, there are very few Spanish Language Models which result to be small and too general. Legal slang could be think of a Spanish variant on its own as it is very complicated in vocabulary, semantics and phrase understanding. For this work we gathered legal-domain corpora from different sources, generated a model and evaluated against Spanish general domain tasks. The model provides reasonable results in those tasks.

We modeled the Quora question pairs dataset to identify a similar question. The dataset that we use is provided by Quora. The task is a binary classification. We tried several methods and algorithms and different approach from previous works. For feature extraction, we used Bag of Words including Count Vectorizer, and Term Frequency-Inverse Document Frequency with unigram for XGBoost and CatBoost. Furthermore, we also experimented with WordPiece tokenizer which improves the model performance significantly. We achieved up to 97 percent accuracy. Code and Dataset.

This paper presents an approach to creating intelligent conversational agents that are capable of returning appropriate responses to natural language input. Our approach consists of using a supervised learning algorithm in combination with different NLP algorithms in training the system to identify paraphrases of the user’s question stored in a database. When tested on a data set consisting of questions and answers for a current conversational agent project, our approach returned an accuracy score of 79.15%, a precision score of 77.58%and a recall score of 78.01%.

In this paper we propose a novel approach to the task of paraphrase identification. The proposed approach quantifies both the similarity and dissimilarity between two sentences. The similarity and dissimilarity is assessed based on lexico-semantic information, i.e., word semantics, and syntactic information in the form of dependencies, which are explicit syntactic relations between words in a sentence. Word semantics requires mapping words onto concepts in a taxonomy and then using word-to-word similarity metrics to compute their semantic relatedness. Dependencies are obtained using state-of-the-art dependency parsers. One important aspect of our approach is the weighting of missing dependencies, i.e., syntactic relations present in one sentence but not the other. We report experimental results on the Microsoft Paraphrase Corpus, a standard data set for evaluating approaches to paraphrase identification. The experiments showed that the proposed approach offers state-of-the-art results. In particular, our approach offers better precision when compared to other state-of-the-art systems.