Semantic parsing using hierarchical representations has recently been proposed for task oriented dialog with promising results [ 2018]. In this paper, we present three different improvements to the model: contextualized embeddings, ensembling, and pairwise re-ranking based on a language model. We taxonomize the errors possible for the hierarchical representation, such as wrong top intent, missing spans or split spans, and show that the three approaches correct different kinds of errors. The best model combines the three techniques and gives 6.4 better exact match accuracy than the state-of-the-art, with an error reduction of 33 , resulting in a new state-of-the-art result on the Task Oriented Parsing (TOP) dataset.
Within both of these areas, neural approaches have supplanted previous feature-engineering based approaches in recent years @cite_22 @cite_17 . In the context of tree-structured semantic parsing, some other interesting approaches include Seq2Tree @cite_12 which modifies the standard Seq2Seq decoder to better output trees; SCANNER @cite_23 @cite_13 which extends the RNNG formulation specifically for semantic parsing such that the output is no longer coupled with the input; and TRANX @cite_7 and Abstract Syntax Network @cite_2 which generate code along a programming language schema. For graph-structured semantic parsing @cite_15 @cite_3 , SLING @cite_4 produces graph-structured parses by modeling semantic parsing as a neural transition parsing problem with a more expressive transition tag set. While graph structures can provide more detailed semantics, they are more difficult to parse and can be an overkill for understanding task oriented utterances.
@cite_4 @cite_22 @cite_7 @cite_3 @cite_23 @cite_2 @cite_15 @cite_13 @cite_12 @cite_17
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Neural probabilistic parsers are attractive for their capability of automatic feature combination and small data sizes. A transition-based greedy neural parser has given better accuracies over its linear counterpart. We propose a neural probabilistic structured-prediction model for transition-based dependency parsing, which integrates search and learning. Beam search is used for decoding, and contrastive learning is performed for maximizing the sentence-level log-likelihood. In standard Penn Treebank experiments, the structured neural parser achieves a 1.8 accuracy improvement upon a competitive greedy neural parser baseline, giving performance comparable to the best linear parser. We present structured perceptron training for neural network transition-based dependency parsing. We learn the neural network representation using a gold corpus augmented by a large number of automatically parsed sentences. Given this fixed network representation, we learn a final layer using the structured perceptron with beam-search decoding. On the Penn Treebank, our parser reaches 94.26 unlabeled and 92.41 labeled attachment accuracy, which to our knowledge is the best accuracy on Stanford Dependencies to date. We also provide indepth ablative analysis to determine which aspects of our model provide the largest gains in accuracy. This article considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence. The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account. We introduce a new method for the reranking task, based on the boosting approach to ranking problems described in (1998). We apply the boosting method to parsing the Wall Street Journal treebank. The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model. The new model achieved 89.75 F-measure, a 13 relative decrease in F measure error over the baseline model's score of 88.2 . The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. Experiments show significant efficiency gains for the new algorithm over the obvious implementation of the boosting approach. We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work on feature selection methods within log-linear (maximum-entropy) models. Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation. We present structured perceptron training for neural network transition-based dependency parsing. We learn the neural network representation using a gold corpus augmented by a large number of automatically parsed sentences. Given this fixed network representation, we learn a final layer using the structured perceptron with beam-search decoding. On the Penn Treebank, our parser reaches 94.26 unlabeled and 92.41 labeled attachment accuracy, which to our knowledge is the best accuracy on Stanford Dependencies to date. We also provide in-depth ablative analysis to determine which aspects of our model provide the largest gains in accuracy.