Which content coverage constraints do they design?
Answer with content missing: (Content Coverage Constraint section) We thus devise an additional learning constraint based on the nature of content description—each data tuple in the content record should usually be mentioned exactly once in the generated sentence.
The copy mechanism over content record x enables a simple yet effective way to encourage the behavior. Intuitively, we want each tuple to be copied once and only once on average.
Controlled generation of text is of high practical use. Recent efforts have made impressive progress in generating or editing sentences with given textual attributes (e.g., sentiment). This work studies a new practical setting of text content manipulation. Given a structured record, such as `(PLAYER: Lebron, POINTS: 20, ASSISTS: 10)', and a reference sentence, such as `Kobe easily dropped 30 points', we aim to generate a sentence that accurately describes the full content in the record, with the same writing style (e.g., wording, transitions) of the reference. The problem is unsupervised due to lack of parallel data in practice, and is challenging to minimally yet effectively manipulate the text (by rewriting/adding/deleting text portions) to ensure fidelity to the structured content. We derive a dataset from a basketball game report corpus as our testbed, and develop a neural method with unsupervised competing objectives and explicit content coverage constraints. Automatic and human evaluations show superiority of our approach over competitive methods including a strong rule-based baseline and prior approaches designed for style transfer.
Generating natural language text to describe structured content, such as a database record or a table, is of ubiquitous use in real-life applications including data report generation BIBREF0 , article writing BIBREF1 , BIBREF2 , dialog systems BIBREF3 , BIBREF4 , and many others. Recent efforts have developed many techniques to improve fidelity to the source content, such as new powerful neural architectures BIBREF5 , BIBREF6 , hybrid generation and retrieval BIBREF7 , BIBREF8 , and so forth, most of which are applied in supervised context. Language is rich with variation–given a data record, there are diverse possible ways of saying the same content, with different word choices, expressions, transitions, tone, etc. Previous data-to-text work has largely focused only on content fidelity, while ignoring and lacking control over the rich stylistic properties of language. It can be practically useful to generate text that is not only describing the conditioning content, but also following a designated writing style, e.g., as provided in a piece of reference text. In this work, we study the new yet practical problem in which we aim to express given content with a sentence and mimic the writing style of a reference sentence (Table TABREF1 ). More specifically, we are given a structured data record containing the content to describe, along with a sentence about a similar but different matter. Our goal is to generate a new sentence that precisely depicts all content in the record, while at the same time using as much of the writing style of reference sentence as possible. As above, the problem differs critically from the supervised data-to-text BIBREF0 or retrieval-and-rewriting work BIBREF7 , BIBREF8 as we have imposed an additional goal of preserving the reference text style. The resulting problem is typically unsupervised due to lack of parallel data. The problem also differs in important ways from the emerging task of text style transfer BIBREF9 , BIBREF10 which assumes an existing sentence of certain content, and modifies single or multiple textual attributes of the sentence (e.g., transferring negative sentiment to positive) without changing the content. Our task, on the contrary, assumes abstract style is encoded in a reference sentence and attempts to modify its concrete content to express new information from the structured record. The different setting can lead to different application scenarios in practice, and pose unique technical challenges. In particular, though the most recent style transfer research BIBREF11 , BIBREF12 has controlled multiple categorical attributes which are largely independent or loosely correlated to each other, a content record in our task, in comparison, can contain varying number of entries which are of different types (e.g., player, points, defensive/offensive rebounds, etc), having many possible values (e.g., hundreds of players), and are structurally coupled (e.g., 32 points by Lebron). A model must understand the content structure, and minimally yet sufficiently manipulate the reference sentence by rewriting, adding, or deleting text portions, with necessary polishing for grammatical correctness and fluency. We name the problem text content manipulation. Our empirical studies show the most recent models designed for style transfer fail to perform well in the task. In this paper, we first develop a large unsupervised dataset as a testbed of the new task. The dataset is derived from an NBA game report corpus BIBREF0 . In each data instance, besides a content record and a reference sentence as the problem inputs, we also collect side information useful for unsupervised learning. Specifically, each instance has an auxiliary sentence that was originally written by human reporters to describe the content record without seeing (and thus stylistically irrelevant to) the reference sentence. We also provide the structured record of the reference sentence. The side information can provide valuable clues for models to understand the content structure and text semantics at training time. We do not rely on the side information at test time. We then propose a neural method to tackle the problem. With a hybrid attention and copy mechanism, the model effectively encodes the reference and faithfully copies content from the record. The model is learned with two competing objectives of reconstructing the auxiliary sentence (for content fidelity) and the reference sentence (for style preservation). We further improve the model with an explicit content coverage constraint which encourages to precisely and fully convey the structured content. For empirical study, we devise automatic metrics to measure content fidelity and style preservation, respectively. We also perform human evaluations to compare different approaches. Results demonstrate the proposed method significantly improves over others, including a strong rule-based baseline and the recent style transfer models.