Structural Embedding of Syntactic Trees for Machine Comprehension
نویسندگان
چکیده
This paper develops a model that addresses syntactic embedding for machine comprehension, a key task of natural language understanding. Our proposed model, structural embedding of syntactic trees (SEST), takes each word in a sentence, constructs a sequence of syntactic nodes extracted from syntactic parse trees, and encodes the sequence into a vector representation. The learned vector is then incorporated into neural attention models, which allows learning the mapping of syntactic structures between question and context pairs. We evaluate our approach on SQuAD dataset and demonstrate that our model can accurately identify the syntactic boundaries of the sentences and to extract answers that are syntactically coherent over the baseline methods.
منابع مشابه
MEMEN: Multi-layer Embedding with Memory Networks for Machine Comprehension
Machine comprehension(MC) style question answering is a representative problem in natural language processing. Previous methods rarely spend time on the improvement of encoding layer, especially the embedding of syntactic information and name entity of the words, which are very crucial to the quality of encoding. Moreover, existing attention methods represent each query word as a vector or use ...
متن کاملClassifier Based Machine Comprehension
In this report we implement a machine comprehension system, and train and test it on the MCTest dataset (Richardson et al., 2013). We treat it as a classification problem. We use baseline features and syntactic features to compute the score for each candidate answers. We also used a set of NLP techniques, including word embedding, coreference resolution and lemmatization to improve the performa...
متن کاملThe Effect of Reducing Lexical and Syntactic Complexity of Texts on Reading Comprehension
The present study investigated the effect of different types of text simplification (i.e., reducing the lexical and syntactic complexity of texts) on reading comprehension of English as a Foreign Language learners (EFL). Sixty female intermediate EFL learners from three intact classes in Tabarestan Language Institute in Tehran participated in the study. The intact classes were assigned to three...
متن کاملمدل ترجمه عبارت-مرزی با استفاده از برچسبهای کمعمق نحوی
Phrase-boundary model for statistical machine translation labels the rules with classes of boundary words on the target side phrases of training corpus. In this paper, we extend the phrase-boundary model using shallow syntactic labels including POS tags and chunk labels. With the priority of chunk labels, the proposed model names non-terminals with shallow syntactic labels on the boundaries of ...
متن کاملTree-based Hybrid Machine Translation
I present an automatic post-editing approach that combines translation systems which produce syntactic trees as output. The nodes in the generation tree and targetside SCFG tree are aligned and form the basis for computing structural similarity. Structural similarity computation aligns subtrees and based on this alignment, subtrees are substituted to create more accurate translations. Two diffe...
متن کامل