Generating natural language descriptions for the structured tables which consist of multiple attribute-value tuples is a convenient way to help people to understand the tables. Most neural table-to-text models are based on the encoder-decoder framework. However, it is hard for a vanilla encoder to learn the accurate semantic representation of a complex table. The challenges are two-fold: firstly, the table-to-text datasets often contain large number of attributes across different domains, thus it is hard for the encoder to incorporate these heterogeneous resources. Secondly, the single encoder also has difficulties in modeling the complex attribute-value structure of the tables. To this end, we first propose a two-level hierarchical encoder with coarse-to-fine attention to handle the attribute-value structure of the tables. Furthermore, to capture the accurate semantic representations of the tables, we propose 3 joint tasks apart from the prime encoder-decoder learning, namely auxiliary sequence labeling task, text autoencoder and multi-labeling classification, as the auxiliary supervisions for the table encoder. We test our models on the widely used dataset WIKIBIO which contains Wikipedia infoboxes and related descriptions. The dataset contains complex tables as well as large number of attributes across different domains. We achieve the state-of-the-art performance on both automatic and human evaluation metrics.

Xiujun Li, Chunyuan Li, Qiaolin Xia, Yonatan Bisk, Asli Celikyilmaz, Jianfeng Gao, Noah A. Smith, Yejin Choi. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Word Sense Disambiguation (WSD) aims to identify the correct meaning of polysemous words in the particular context.Lexical resources like WordNet which are proved to be of great help for WSD in the knowledge-based methods.However, previous neural networks for WSD always rely on massive labeled data (context), ignoring lexical resources like glosses (sense definitions).In this paper, we integrate the context and glosses of the target word into a unified framework in order to make full use of both labeled data and lexical knowledge.Therefore, we propose GAS: a gloss-augmented WSD neural network which jointly encodes the context and glosses of the target word.GAS models the semantic relationship between the context and the gloss in an improved memory network framework, which breaks the barriers of the previous supervised methods and knowledge-based methods.We further extend the original gloss of word sense via its semantic relations in WordNet to enrich the gloss information.The experimental results show that our model outperforms the state-of-theart systems on several English all-words WSD datasets.

Although marketing researchers and sociologists have recognized the importance of buying decision process and its significant influence on consumer's purchasing behaviors, existing recommender systems do not explicitly model the consumer buying decision process or capture the sequential regularities of what happens before and after each purchase. In this paper, we try to bridge the gap and improve recommendation systems by explicitly modeling consumer buying decision process and corresponding stages. In particular, we propose a multi-task learning model with long short-term memory networks (LSTM) to learn consumer buying decision process. It maps items, users, product categories, and the behavior sequences into real valued vectors, with which the probability of purchasing a product can be estimated. In this way, the model can capture user intentions and preferences, predicts the conversion rate of each candidate product, and makes recommendations accordingly. Experiments on real world data demonstrate the effectiveness of the proposed approach.

The goal of Word Sense Disambiguation (WSD) is to identify the correct meaning of a word in the particular context.Traditional supervised methods only use labeled data (context), while missing rich lexical knowledge such as the gloss which defines the meaning of a word sense.Recent studies have shown that incorporating glosses into neural networks for WSD has made significant improvement.However, the previous models usually build the context representation and gloss representation separately.In this paper, we find that the learning for the context and gloss representation can benefit from each other.Gloss can help to highlight the important words in the context, thus building a better context representation.Context can also help to locate the key words in the gloss of the correct word sense.Therefore, we introduce a co-attention mechanism to generate co-dependent representations for the context and gloss.Furthermore, in order to capture both word-level and sentence-level information, we extend the attention mechanism in a hierarchical fashion.Experimental results show that our model achieves the state-of-the-art results on several standard English all-words WSD test datasets.

Previous studies on Chinese semantic role labeling (SRL) have concentrated on a single semantically annotated corpus.But the training data of single corpus is often limited.Whereas the other existing semantically annotated corpora for Chinese SRL are scattered across different annotation frameworks.But still, Data sparsity remains a bottleneck.This situation calls for larger training datasets, or effective approaches which can take advantage of highly heterogeneous data.In this paper, we focus mainly on the latter, that is, to improve Chinese SRL by using heterogeneous corpora together.We propose a novel progressive learning model which augments the Progressive Neural Network with Gated Recurrent Adapters.The model can accommodate heterogeneous inputs and effectively transfer knowledge between them.We also release a new corpus, Chinese Sem-Bank, for Chinese SRL 1 .Experiments on CPB 1.0 show that our model outperforms state-of-the-art methods.