Table-to-text generation aims to generate a description for a factual table which can be viewed as a set of field-value records. To encode both the content and the structure of a table, we propose a novel structure-aware seq2seq architecture which consists of field-gating encoder and description generator with dual attention. In the encoding phase, we update the cell memory of the LSTM unit by a field gate and its corresponding field value in order to incorporate field information into table representation. In the decoding phase, dual attention mechanism which contains word level attention and field level attention is proposed to model the semantic relevance between the generated description and the table. We conduct experiments on the WIKIBIO dataset which contains over 700k biographies and corresponding infoboxes from Wikipedia. The attention visualizations and case studies show that our model is capable of generating coherent and informative descriptions based on the comprehensive understanding of both the content and the structure of a table. Automatic evaluations also show our model outperforms the baselines by a great margin. Code for this work is available on this https URL
Many approaches have been proposed to learn the individual modules. For content selection module, one approach builds a content selection model by aligning records and sentences @cite_16 @cite_2 . A hierarchical semi-Markov method is proposed by @cite_1 which first associates the text sequences to corresponding records and then generates corresponding descriptions from these records. Surface realization is often treated as a concept-to-text generation task from a given representation. reiter2000building , walker2001spot and stent2004trainable utilize various linguistic features to train sentence planners for sentence generation. Context-free grammars are also used to generate natural language sentences from formal meaning representations @cite_0 @cite_8 . Other effective approaches include hybrid alignment tree @cite_6 , tree conditional random fields @cite_17 , tree adjoining grammar @cite_11 and template extraction in a log-linear framework @cite_20 . Recent work combines content selection and surface realization in a unified framework @cite_14 @cite_9 @cite_10 @cite_7
@cite_11 @cite_14 @cite_7 @cite_8 @cite_9 @cite_1 @cite_6 @cite_0 @cite_2 @cite_16 @cite_10 @cite_20 @cite_17
2107288097 2035585180 2121127625 2057589672
This paper proposes a data-driven method for concept-to-text generation, the task of automatically producing textual output from non-linguistic input. A key insight in our approach is to reduce the tasks of content selection ("what to say") and surface realization ("how to say") into a common parsing problem. We define a probabilistic context-free grammar that describes the structure of the input (a corpus of database records and text describing some of them) and represent it compactly as a weighted hypergraph. The hypergraph structure encodes exponentially many derivations, which we rerank discriminatively using local and global features. We propose a novel decoding algorithm for finding the best scoring derivation and generating in this setting. Experimental evaluation on the Atis domain shows that our model outperforms a competitive discriminative system both using BLEU and in a judgment elicitation study. levels, such as Pet6fi's text grammar and Rumelhart's schemata for stories. By defining permissible interrelations between constituents of larger texts at all levels of abstraction, these create meaning structures organized on both hierarchical and associative principles, rather than on a sequential basis. The automated "understanding" process consists of instantiating an appropriate set of event templates by associating a given text with the ERGO inventory of event templates and const ucting a network of instanti ted templates based on intertemplate relational rules and constraints. The network of instantiated t mplates thus represents the information content of the unstructured original text, and provides the structured input for the event record data bas . Details of the automated understanding process presented here in the abstract are given in the follo ing section. 3.2 Processing Principles. The process of data base generation involves two major functions: (1) co tent analysis of the incoming text (2) event record synthesis (or production) The first involves constructing a meaning representation of the text and the second the extraction of relevant information and its storage in a data base record. The major focus of ERGO is on reports of a particular class of events which describe aircraft movements. The unit of analysis is therefore the report, a textual unit consisting of one or more par graphs, each containing one or more sentences. The first step in the analytical process involves a lexical lookup: a lexical entry contains morphological, syntactic and semantic information, on the lines of Sager (1973) and (1973). Each sentence is then subjected to a syntactic analysis by means of an Augmented Transition Network (ATN) parser (Woods, 1970). Since event templates are based on propositional structures, the analytical 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. In this paper we present a scheme to select relevant subsets of sentences from a large generic corpus such as text acquired from the web. A relative entropy (R.E) based criterion is used to incrementally select sentences whose distribution matches the domain of interest. Experimental results show that by using the proposed subset selection scheme we can get significant performance improvement in both Word Error Rate (WER) and Perplexity (PPL) over the models built from the entire web-corpus by using just 10 of the data. In addition incremental data selection enables us to achieve significant reduction in the vocabulary size as well as number of n-grams in the adapted language model. To demonstrate the gains from our method we provide a comparative analysis with a number of methods proposed in recent language modeling literature for cleaning up text.