Definition Modelling for Appropriate Specificity (2021)

Abstract: Definition generation techniques aim to generate a definition of a target word or phrase given a context. In previous studies, researchers have faced various issues such as the out-of-vocabulary problem and over/under-specificity problems. Over-specific definitions present narrow word meanings, whereas under-specific definitions present general and context-insensitive meanings. Herein, we propose a method for definition generation with appropriate specificity. The proposed method addresses the aforementioned problems by leveraging a pre-trained encoder-decoder model, namely Text-to-Text Transfer Transformer, and introducing a re-ranking mechanism to model specificity in definitions. Experimental results on standard evaluation datasets indicate that our method significantly outperforms the previous state-of-the-art method. Moreover, manual evaluation confirms that our method effectively addresses the over/under-specificity problems.

## introduction the usage of a word or phrase changes over time and new words and phrases emerge every day; therefore, the maintenance of their meanings in dictionaries is crucial but labour-intensive and timeconsuming. such definitions are also useful for computer-aided language learning (call), which helps language learners learn a target word or phrase @xcite . a definition generation technique aims to automatically generate a textual definition for a target word or phrase (referred to as 'target' herein) in a given sentence containing the target (referred to as 'local context' herein). @xcite employed a static word embedding that models the usage of a target word or phrase, and @xcite local context health professionals are mobilising to condemn the government , propose major structural reforms , and hammer the ineffectual minister . proposed method attack or criticize severely target bang local context young andrew wilson , until a bang on the head necessitated his withdrawal , again played very well . reference a sudden painful blow @xcite ( of a person ) strike or strike ( something ) with a sudden sharp noise proposed method a sudden sharp blow an under-specific definition denotes a general definition wherein part of the meaning of the target word in context is lost. in an over-specific definition represents a definition that contains too many details, which narrow down the meaning more than what the target truly represents. in we evaluate our method on four commonly used datasets for definition generation. the results indicated that our method increased bleu points from 2.28 to 7.95 and nist points from 7.31 to 35.95 in comparison with those of previous state-of-the-art methods. furthermore, a manual evaluation confirmed that our method reduced 4.0% and 0.5% of under-and over-specific definitions produced by the t5 model, respectively. ## related work an early study on definition generation @xcite proposed a method that uses pretrained word embeddings as global contexts of a target. owing to the lack of local contexts, this previous method cannot generate an appropriate definition for polysemous words. in contrast, @xcite proposed a method that considers only the local context of a target using a wordlevel encoder for encoding the context to generate definitions of internet slang. the following studies considered an approach that combines the global and local contexts of a target. @xcite proposed the first model that utilises both global and local contexts to disambiguate polysemous words. @xcite advanced this approach and proposed a method that models local and global contexts with multiple encoders and gate mechanisms. @xcite exploited lexical semantic relations between the target and words in definitions. following @xcite , @xcite further introduced a module to decompose the meanings of words as discrete latent variables. furthermore, @xcite established a transformer-based model for generating chinese definitions, followed by @xcite , who use the attention-based model with glove vectors @xcite in english definition modelling. nevertheless, all these studies struggle with the oov problem. moreover, the encoder-decoder models used in these studies were trained on relatively small corpora for definition modelling. therefore, these previous studies often result in oov definitions, i.e., 'a target is unk ,' particularly for non-standard languages (e.g., internet slang). @xcite employed the pretrained bart @xcite for definition generation to address the problem. furthermore, these studies do not have any mechanism to consider the specificity of the generated definitions. although these models succeeded in generating definitions without oov, the generated definitions are often too general or too specific. to this end, we employ the t5 model pre-trained on a large-scale corpora, which effectively address the oov problem. furthermore, we address the over/under-specific definition problem using the re-ranking mechanism. ## proposed model the overview of the proposed method is shown in figure 1 . first, we generate n-best definitions using beam search on a fine-tuned t5 model, as described in section 3.1. then, we obtain re-ranking scores for these definitions using two additional t5 models, as presented in section 3.2. specifically, we re-rank definitions based on the generation likelihood, generality, and specificity. lastly, we assemble these scores to establish a re-ranking mechanism for identifying a definition with appropriate specificity, as described in section 3.3. ## experimental setup we compared the performance of the proposed method with those of previous state-of-the-art methods using the standard datasets for automatic definition generation. this section describes the experimental setup in detail. ## experimental results and analyses we present the results of the automatic evaluation and further conduct quantitative analyses to declare the performance of the proposed method under different conditions. 13 furthermore, we conduct a manual analysis to investigate whether the over/under-specific definition problems are addressed. ## conclusion we addressed the definition generation problem and developed a re-ranking mechanism equipped with a pre-trained t5 model. the quantitative and qualitative analyses confirmed that the proposed method significantly outperformed previous stateof-the-art methods and the strong fine-tuned t5 model and successfully generated definitions with appropriate specificity. as future work, we aim to investigate the effectiveness of the proposed method for cross-lingual definition generation.