Task oriented dialog systems typically first parse user utterances to semantic frames comprised of intents and slots. Previous work on task oriented intent and slot-filling work has been restricted to one intent per query and one slot label per token, and thus cannot model complex compositional requests. Alternative semantic parsing systems have represented queries as logical forms, but these are challenging to annotate and parse. We propose a hierarchical annotation scheme for semantic parsing that allows the representation of compositional queries, and can be efficiently and accurately parsed by standard constituency parsing models. We release a dataset of 44k annotated queries (fb.me semanticparsingdialog), and show that parsing models outperform sequence-to-sequence approaches on this dataset.
While we used a tree-structured representation, others have used arbitrary graphs, such as Abstract Meaning Representation @cite_1 and Alexa Meaning Representation @cite_3 . These approaches can represent complex constructions that are beyond the scope of our approach, but with significantly challenging parsing @cite_6 . We showed that such cases are very rare in our data.
@cite_1 @cite_6 @cite_3
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We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-to-graph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual availability of an AMR-to-text generator. The framework is data-driven, trainable, and not specifically designed for a particular domain. Experiments on gold-standard AMR annotations and system parses show promising results. Code is available at: this https URL Abstract Theories of lexical representation in production provide sophisticated accounts of the way in which information is activated during lexical access (e.g., Levelt, Roelofs, & Meyer, 1999 ), but there has been little attempt to account for the way in which the structure of the lexical entry affects the formulation processes that underlie the production of complex expressions. This paper first outlines such an account, and then reports three experiments that investigated the priming of noun-phrase structure in dialogue. Experiment 1 showed that speakers used a complex noun phrase containing a relative clause (e.g., “the square that’s red”) more often after hearing a syntactically similar noun phrase than after hearing a simple noun phrase, and that this effect was enhanced when the head noun (“square”) was repeated. Experiment 2 showed an enhanced priming effect when prime and target contained semantically related nouns (e.g., “goat” and “sheep”). Experiment 3 showed no enhanced effect when prime and target bore a close phonological relationship (e.g., “ship” and “sheep”). These results provide support for our account, and suggest that syntactic encoding may be unaffected by phonological feedback. We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inference in discourse. For example, when we see the relation person on bike', it is natural to replace on' with ride' and infer person riding bike on a road' even the road' is not evident. Therefore, exploiting such bias as a language prior is expected to help the conventional encoder-decoder models less likely overfit to the dataset bias and focus on reasoning. Specifically, we use the scene graph --- a directed graph ( @math ) where an object node is connected by adjective nodes and relationship nodes --- to represent the complex structural layout of both image ( @math ) and sentence ( @math ). In the textual domain, we use SGAE to learn a dictionary ( @math ) that helps to reconstruct sentences in the @math pipeline, where @math encodes the desired language prior; in the vision-language domain, we use the shared @math to guide the encoder-decoder in the @math pipeline. Thanks to the scene graph representation and shared dictionary, the inductive bias is transferred across domains in principle. We validate the effectiveness of SGAE on the challenging MS-COCO image captioning benchmark, e.g., our SGAE-based single-model achieves a new state-of-the-art @math CIDEr-D on the Karpathy split, and a competitive @math CIDEr-D (c40) on the official server even compared to other ensemble models. We present a two-stage framework to parse a sentence into its Abstract Meaning Representation (AMR). We first use a dependency parser to generate a dependency tree for the sentence. In the second stage, we design a novel transition-based algorithm that transforms the dependency tree to an AMR graph. There are several advantages with this approach. First, the dependency parser can be trained on a training set much larger than the training set for the tree-to-graph algorithm, resulting in a more accurate AMR parser overall. Our parser yields an improvement of 5 absolute in F-measure over the best previous result. Second, the actions that we design are linguistically intuitive and capture the regularities in the mapping between the dependency structure and the AMR of a sentence. Third, our parser runs in nearly linear time in practice in spite of a worst-case complexity ofO(n 2 ).