Explicit Semantic Decomposition for Definition Generation (2020)

Abstract: Definition generation, which aims to automatically generate dictionary definitions for words, has recently been proposed to assist the construction of dictionaries and help people understand unfamiliar texts. However, previous works hardly consider explicitly modeling the “components” of definitions, leading to under-specific generation results. In this paper, we propose ESD, namely Explicit Semantic Decomposition for definition Generation, which explicitly decomposes the meaning of words into semantic components, and models them with discrete latent variables for definition generation. Experimental results show that achieves top results on WordNet and Oxford benchmarks, outperforming strong previous baselines.

## introduction dictionary definition, which provides explanatory sentences for word senses, plays an important role in natural language understanding for human. it is a common practice for human to consult a dictionary when encountering unfamiliar words @xcite . however, it is often the case that we cannot find satisfying definitions for words that are rarely used or newly created. to assist dictionary compilation and help human readers understand unfamiliar texts, generating definitions automatically is of practical significance. @xcite first propose definition modeling, which is the task of generating the dictionary definition for a given word with its embedding. @xcite extend the work by incorporating word sense disambiguation to generate context-aware word definitions.both methods adopt a variant of encoder-decoder architecture, the person in charge of a ship generated the person who is a member of a ship although the existing encoder-decoder architecture @xcite @xcite yields reasonable generation results, it relies heavily on the decoder to extract thorough semantic components of the word, leading to under-specific definition generation results, i.e. missing some semantic components. as illustrated in linguists and lexicographers define a word by decomposing its meaning into its semantic components and expressing them in natural language sentences @xcite . inspired by this, @xcite incorporate sememes @xcite , i.e. minimum units of semantic meanings of human languages, in the task of generating definition in chinese. however, it is just as, if not more, time-consuming and expensive to label the components of words than to write definitions manually. in this paper, we propose to explicitly decom-pose the meaning of words into semantic components for definition generation. we introduce a group of discrete latent variables to model the underlying semantic components.extending the established training technique for discrete latent variable used in representation learning @xcite and machine translation tasks @xcite @xcite , we further propose two auxiliary losses to ensure that the introduced latent variables capture the word semantics. experimental results show that our method achieves significant improvements over previous methods on two definition generation datasets. we also show that our model indeed learns meaningful and informative latent codes, and generates more precise and specific definitions. ## background in this section, we introduce the background of the original definition modeling task and two extensive works to original definition modeling. ## approach in this section, we present esd, namely explicit semantic decomposition for context-aware definition generation. ## analysis to gain more insight into the improvement provided by the proposed method, we perform several analyses in this section. ## related work definition generation definition modeling was firstly proposed by @xcite . they take a word embedding as input and generate a definition of the word. an obvious drawback is that their model cannot handle polysemous words. recently several works @xcite @xcite consider the context-aware definition generation task, where the context is introduced to disambiguate senses of words. they all adopt a encoder-decoder architecture, and rely heavily on the decoder to extract semantic components of the word semantic, thus leading to under-specific definitions. in contrast, we introduce a group of discrete latent variables to model these semantic components explicitly. ## conclusion we proposed esd, a context-aware definition generation model that explicitly models the decomposed semantics of words. specifically, we model the decomposed semantics as discrete latent variables, and training with auxiliary losses to ensure that the model learns informative latent codes for definition modeling. as a result, esd leads to significant improvements over the previous strong baselines on two established definition datasets. quantitative and qualitative analysis showed that our model could generate more meaningful, specific and accurate definitions. in future work, we plan to seek better ways to guide the learning of latent variables, such as using dynamic routing @xcite method to align the latent variables and sememes, and learn more explainable latent codes.