Recurrent neural network grammars (RNNG) are a recently proposed probabilistic generative modeling family for natural language. They show state-of-the-art language modeling and parsing performance. We investigate what information they learn, from a linguistic perspective, through various ablations to the model and the data, and by augmenting the model with an attention mechanism (GA-RNNG) to enable closer inspection. We find that explicit modeling of composition is crucial for achieving the best performance. Through the attention mechanism, we find that headedness plays a central role in phrasal representation (with the model's latent attention largely agreeing with predictions made by hand-crafted head rules, albeit with some important differences). By training grammars without nonterminal labels, we find that phrasal representations depend minimally on nonterminals, providing support for the endocentricity hypothesis.
The problem of understanding neural network models in NLP has been previously studied for sequential RNNs @cite_11 @cite_16 . showed that sequence-to-sequence neural translation models capture a certain degree of syntactic knowledge of the source language, such as voice (active or passive) and tense information, as a by-product of the translation objective. Our experiment on the importance of composition function was motivated by and , who achieved competitive parsing accuracy without explicit composition. In another work, investigated the importance of recursive tree structures (as opposed to linear recurrent models) in four different tasks, including sentiment and semantic relation classification. Their findings suggest that recursive tree structures are beneficial for tasks that require identifying long-range relations, such as semantic relationship classification, with no conclusive advantage for sentiment classification and discourse parsing. Through the stack-only ablation we demonstrate that the RNNG composition function is crucial to obtaining state-of-the-art parsing performance. Extensive prior work on phrase-structure parsing typically employs the probabilistic context-free grammar formalism, with lexicalized @cite_2 and nonterminal @cite_5 @cite_19 augmentations. The conjecture that fine-grained nonterminal rules and labels can be discovered given weaker bracketing structures was based on several studies @cite_18 @cite_6 @cite_12 .
@cite_18 @cite_6 @cite_19 @cite_2 @cite_5 @cite_16 @cite_12 @cite_11
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Recurrent neural nets (RNN) and convolutional neural nets (CNN) are widely used on NLP tasks to capture the long-term and local dependencies, respectively. Attention mechanisms have recently attracted enormous interest due to their highly parallelizable computation, significantly less training time, and flexibility in modeling dependencies. We propose a novel attention mechanism in which the attention between elements from input sequence(s) is directional and multi-dimensional (i.e., feature-wise). A light-weight neural net, "Directional Self-Attention Network (DiSAN)", is then proposed to learn sentence embedding, based solely on the proposed attention without any RNN CNN structure. DiSAN is only composed of a directional self-attention with temporal order encoded, followed by a multi-dimensional attention that compresses the sequence into a vector representation. Despite its simple form, DiSAN outperforms complicated RNN models on both prediction quality and time efficiency. It achieves the best test accuracy among all sentence encoding methods and improves the most recent best result by 1.02 on the Stanford Natural Language Inference (SNLI) dataset, and shows state-of-the-art test accuracy on the Stanford Sentiment Treebank (SST), Multi-Genre natural language inference (MultiNLI), Sentences Involving Compositional Knowledge (SICK), Customer Review, MPQA, TREC question-type classification and Subjectivity (SUBJ) datasets. Recurrent neural networks (RNN) have gained a reputation for beating state-of-the-art results on many NLP benchmarks and for learning representations of words and larger linguistic units that encode complex syntactic and semantic structures. However, it is not straight-forward to understand how exactly these models make their decisions. Recently (2015) developed methods to provide linguistically motivated analysis for RNNs trained for sentiment analysis. Here we focus on the analysis of a multi-modal Gated Recurrent Neural Network (GRU) architecture trained to predict image-vectors extracted from images using a CNN trained on ImageNet from their corresponding descriptions. We propose two methods to explore the importance of grammatical categories with respect to the model and the task. We observe that the model pays most attention to head-words, noun subjects and adjectival modifiers and least to determiners and coordinations. Neural networks with recurrent or recursive architecture have shown promising results on various natural language processing (NLP) tasks. The recurrent and recursive architectures have their own strength and limitations. The recurrent networks process input text sequentially and model the conditional transition between word tokens. In contrast, the recursive networks explicitly model the compositionality and the recursive structure of natural language. Current recursive architecture is based on syntactic tree, thus limiting its practical applicability in different NLP applications. In this paper, we introduce a class of tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic tree-based recursive models. NTI constructs a full n-ary tree by processing the input text with its node function in a bottom-up fashion. Attention mechanism can then be applied to both structure and different forms of node function. We demonstrated the effectiveness and the flexibility of a binary-tree model of NTI, showing the model achieved the state-of-the-art performance on three different NLP tasks: natural language inference, answer sentence selection, and sentence classification. Recursive neural networks comprise a class of architecture that can operate on structured input. They have been previously successfully applied to model com-positionality in natural language using parse-tree-based structural representations. Even though these architectures are deep in structure, they lack the capacity for hierarchical representation that exists in conventional deep feed-forward networks as well as in recently investigated deep recurrent neural networks. In this work we introduce a new architecture — a deep recursive neural network (deep RNN) — constructed by stacking multiple recursive layers. We evaluate the proposed model on the task of fine-grained sentiment classification. Our results show that deep RNNs outperform associated shallow counterparts that employ the same number of parameters. Furthermore, our approach outperforms previous baselines on the sentiment analysis task, including a multiplicative RNN variant as well as the recently introduced paragraph vectors, achieving new state-of-the-art results. We provide exploratory analyses of the effect of multiple layers and show that they capture different aspects of compositionality in language.