Controllable Dictionary Example Generation: Generating Example Sentences for Specific Targeted Audiences (2022)

Abstract: Example sentences for targeted words in a dictionary play an important role to help readers understand the usage of words. Traditionally, example sentences in a dictionary are usually created by linguistics experts, which are labor-intensive and knowledge-intensive. In this paper, we introduce the problem of dictionary example sentence generation, aiming to automatically generate dictionary example sentences for targeted words according to the corresponding definitions. This task is challenging especially for polysemous words, because the generated sentences need to reflect different usages and meanings of these targeted words. Targeted readers may also have different backgrounds and educational levels. It is essential to generate example sentences that can be understandable for different backgrounds and levels of audiences. To solve these problems, we propose a controllable target-word-aware model for this task. Our proposed model can generate reasonable examples for targeted words, even for polysemous words. In addition, our model allows users to provide explicit control over attributes related to readability, such as length and lexical complexity, thus generating suitable examples for targeted audiences. Automatic and human evaluations on the Oxford dictionary dataset show that our model can generate suitable examples for targeted words with specific definitions while meeting the desired readability.

## introduction a dictionary usually consists of targeted words, part-of-speech (pos) tags, definitions and corresponding example sentences. definitions and their corresponding examples enable audiences to better master new words, understand unfamiliar texts and the usage of the words in typical sentences, where a definition is a simple description for the meaning of the targeted word, and an example shows audiences how to use the word under this definition. both definitions and examples are critical, playing an important role in language acquisition and natural language understanding. however, it is often the case that audiences cannot find satisfactory example sentences for rarely used or newly coined words. on the other hand, it is time-consuming for experts to create dictionary examples for these words. with the advancement of ai technologies, it is a natural direction to study how to generate dictionary examples automatically, to assist dictionary compilation and help humans understand the corresponding targeted words. dictionary example sentence generation aims to generate example sentences for targeted words to reflect their definitions and usages automatically. recently, definition generation @xcite @xcite has been extensively studied, yet generating example sentences is not well-studied. to the best of our knowledge, we are the first group to introduce this challenging problem. one main challenge for this task is that targeted words must appear in outputs. another challenge is that polysemous words (e.g. 'bank'), which have multiple senses, even multiple pos tags, are ubiquitous. thus, a polysemous word in generated examples should convey the given sense and pos tag. lexically constrained text generation is meant to incorporate some specific keywords into outputs, which has been widely studied. previous lexically constrained models inject the given keywords into outputs either by manipulating the decoding process @xcite , or using the keywords as the initial state and refining it with a series of actions, such as insertion and replacement until it is completed @xcite . it is natural to use these lexically constrained models as baselines, since they have solved the first challenge. in response to the second one, we further extend these lexically constrained models by feeding the definition into the encoder and then injecting the targeted word during decoding. however, these models have two inherent drawbacks for this task: (1) during inference, these models generate a sentence based on the definition and force the targeted word to appear in outputs. however, they fail to explore the correlation between the targeted word and input, thus sacrificing the generation quality to ensure the targeted word appears in outputs. to circumvent these problems, the proposed model is expected to understand this task so that there is no need to interfere with the decoding process. to achieve this goal, we directly feed the targeted word and definition into the model. this simple change brings two advantages over lexically constrained generation models: (1) during training, our model fully explores the correlation between targeted words and definitions, and gradually acquires this task. as a result, even the proposed model does not control decoding, outputs contain targeted words in 99.6% of cases. (2) with the release of control over decoding, our model significantly improves the generation quality and dramatically reduces the inference latency. apart from the above two challenges, the proposed model should generate suitable examples to match the readability levels of different audiences, such as children and college students. to address the third challenge, the proposed model is expected to control the readability-related attributes of outputs, namely length and lexical complexity. inspired by @xcite , the proposed model is trained on discrete control tokens, which are related to the length and lexical complexity of gold example sentences. by doing so, the proposed model will learn to associate the control tokens with the length and lexical complexity of outputs. as a result, we can control the readability of outputs by varying the length and lexical control tokens. our contributions are summarized as follows: (1) we introduce the dictionary example sentence generation task. (2) we propose a large dataset for dictionary example generation. (3) we propose a controllable target-word-aware model and several baselines for this task 1 . (4) we propose two bertbased classifiers to automatically evaluate whether the target word in the generated example conveys the given sense and pos tag, respectively. (5) our ## conclusions in this paper, we first introduce the dictionary example sentence generation problem, and propose a controllable target-word-aware model and several strong baselines for it. we propose two bertbased classifiers to evaluate the definition and pos accuracy of generated examples. our experiment results on the oxford dictionary dataset show that our model outperforms baselines in most metrics and can generate appropriate examples meeting different audiences' understanding levels. models labels training validation pos negative 3,337,293 417,733 positive 1,138,316 141,626 definition negative 4,219,163 532,272 positive 1,138,316 141,626