Bridging the Structural Gap Between Encoding and Decoding for Data-To-Text Generation (2020)

Abstract: Generating sequential natural language descriptions from graph-structured data (e.g., knowledge graph) is challenging, partly because of the structural differences between the input graph and the output text. Hence, popular sequence-to-sequence models, which require serialized input, are not a natural fit for this task. Graph neural networks, on the other hand, can better encode the input graph but broaden the structural gap between the encoder and decoder, making faithful generation difficult. To narrow this gap, we propose DualEnc, a dual encoding model that can not only incorporate the graph structure, but can also cater to the linear structure of the output text. Empirical comparisons with strong single-encoder baselines demonstrate that dual encoding can significantly improve the quality of the generated text.

## introduction data-to-text generation aims to create natural language text to describe the input data @xcite . here we focus on structured text input in a particular form such as a tree or a graph. figure 1 shows an example where the input data is a mini knowledge graph, and the output text is its corresponding natural language description. generating text from such data is helpful for many nlp tasks, such as question answering and dialogue @xcite @xcite . during generation, the structure of the data as well as the content inside the structure jointly determine the generated text. for example, the direction of the edge "capital" in figure 1 determines that "london is the capital of u.k." is an accurate description, but not vice versa. current generation methods are based on sequence-to-sequence (seq2seq) encoder-decoder architecture @xcite , which requires the input data to be serialized as a sequence, resulting in a loss of structural information. recent research has shown the utility of incorporating structural information during generation. by replacing the sequential encoder with a structureaware graph encoder, such as a graph convolutional network (gcns) @xcite or graph-state lstms @xcite , the resulting graph-to-sequence (graph2seq) methods can encode the structural information of the input and thus outperform seq2seq models on certain tasks. however, these architectures broaden the structural gap between the encoder and decoder. that is, while the encoder receives the input data as a graph, the decoder has to create the output text as a linear chain structure. this structural gap increases the difficulty of establishing alignments between source and target, which is believed to play a key role in text generation. for example, in machine translation, pre-reordering the source words into a word order that is close to that of the target sentence can yield significant improvements in translation quality @xcite . this suggests a need for an intermediate "planning" stage @xcite to help with organizing the output. in this work, we present a dual encoding model that is not only aware of the input graph structure but also incorporates a content planning stage. to encode the structural information in the input graph, we use a gcn based graph encoder. to narrow the ensuing structural gap, we use another gcn-based neural planner to create a sequential content plan of this graph, which is represented as a re-ordered sequence of its nodes. the plan is then encoded by an lstm based sequential encoder. during generation, an lstm based decoder simultaneously conditions on the two encoders, which helps it in capturing both the graph structure of the input data and the linear structure of the plan. we expect such a dual encoding (dualenc) structure can integrate the advantages of both graph and sequential encoders while narrowing the structural gap present in single-encoder methods. we evaluate the proposed planning and generation models on the webnlg dataset @xcite -a widely used benchmark for data-to-text generation. experimental results show that our neural planner achieves a 15% absolute improvement on accuracy compared to the previous best planning method. furthermore, dualenc significantly outperforms the previous start-of-the-art on the generation task. the human evaluation confirms that the texts generated by our model are preferred over strong baselines. the contributions of this paper are three-fold: ## problem statement in this work we focus on text generation from rdf data. 1 the input for this task is a set of rdf triples, where each triple (s, p, o) contains a subject, a predicate, and an object. for example, ("u.k.", "capital", "london") is a rdf triple. the output is a natural language text with one or more sentences to describe the facts represented by this graph. figure 1 shows an example of this task. ## dual encoding model for a given input rdf graph, the aim of our method is not only to capture its structural information, but also to facilitate the information alignment between the input and output. the first goal can be achieved by employing a gcn encoder. to achieve the second goal, we first serialize and re-order the nodes of the graph as an intermediate plan using another gcn, and then feed the plan into an lstm encoder. finally, an lstm decoder is used to generate the output by incorporating the context representations of both encoders. notice that the graph and the plan are dual representations of the same input data. we encode them with two independent encoders, which can provide complementary information for decoding. the architecture of our dual encoding method is shown in figure 2 . we describe the two encoders and the decoder in the following three subsections. ## experiments we conduct experiments to evaluate our planner (section 5.2) and the overall generation system (section 5.3). 4 ## conclusion this paper proposes dualenc, a dual encoding method to bridge the structural gap between encoder and decoder for data-to-text generation. we use gcn encoders to capture the structural information of the data, which is essential for accurate planning and faithful generation. we also introduce an intermediate content planning stage to serialize the data and then encode it with an lstm network. this serialized plan is more compatible with the output sequence, making the information alignment between the input and output easier. experiments on webnlg dataset demonstrate the effectiveness of our planner and generator by outperforming the previous state-of-the-art by a large margin. future work will validate the effectiveness of this method on more varied data-to-text generation tasks.