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. 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 (Noraset et al., 2017; Gadetsky et al., 2018; Ishiwatari et al., 2019) 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 (Mou et al., 2015; Hokamp and Liu, 2017), 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 (He and Li, 2021). 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 610 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. (2) These models are computation-intensive, as they need to manipulate the decoding process. 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 Keskar et al. (2019), 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 BERT-based classifiers to automatically evaluate whether the target word in the generated example conveys the given sense and POS tag, respectively. (5) Our 1 Our dataset and code are available at https://github.com/NLPCode/CDEG. experiment results on the Oxford dictionary dataset show that our model outperforms baselines in terms of generation quality, diversity, POS and definition accuracy. More importantly, our model can tailor examples to fit the needs of targeted audiences by controlling the length and lexical complexity. Dictionary Example Sentence Generation aims to generate a fluent example E = { e 1 , . . . , e T } for the targeted word w  under a specific definition D = { d 1 , . . . , d S } , where w  should appear in E and convey D . During training, this task aims to maximize the conditional probability of E : p ( E | w  , D ;  ) = T (cid:89) t =1 p ( e t | e i<t , w  , D ;  ) . (1) 3 Methodology 3.1 Motivation Our motivation is to make the model understand dictionary example sentence generation so that we do not need to interfere with the decoding process. Intuitively, if the model has mastered the requirements of this task, the model will know reasonable outputs should contain the targeted word under the specific sense when seeing the target word and definition. Driven by this motivation, we use an encoder-decoder architecture, initialized with BART (Lewis et al., 2020), where the encoder directly takes the targeted word and definition as inputs. During training, the model gradually learns to incorporate the targeted word under the specific meaning into output, otherwise, it will suffer a large cross-entropy loss between the predicted distributions of the decoder and golden examples. To gain control over the readability of outputs, the model is also trained on the readability-related control tokens of gold examples. In this way, the model will gradually learn to correlate the special token with a readability attribute of outputs, otherwise, it will also suffer a large cross-entropy loss. See Section 3.2 for readability-related control tokens. The overview of the proposed model is shown in Figure 1. The encoder input consists of five parts: the targeted word, POS tag, length, lexical complexity, and definition. Each part begins with a special token, indicating the start of this part. For example, < Word > means the following content is the targeted word. The decoder aims to generate examples based on the encoder inputs. To control the readability of outputs, we need to find out which attributes of outputs are related to readability. Flesch-Kincaid Grade Level (FKGL) and Flesch Reading-Ease Score (FRES) (Kincaid et al., 1975) are widely used to assess the difficulty of English text. Both metrics are related to the average sentence length and assume that the longer the sentence, the more difficult the text is to understand. On the other hand, lexical complexity also affects readability (Shardlow, 2014). For example, too many complicated words appearing in a text may hinder audiences' understanding of the text. Length (Len). Len denotes the number of tokens in a tokenized 2 example. Figure 2 (d) shows that example lengths range from 3 to 60. Hence, we add 58 learnable Len control tokens to the vocabulary. Lexical Complexity (LC). Word frequencies are the most reliable predictor of word complexity (Paetzold and Specia, 2016). Given this, we use word frequencies as a proxy of LC. In the following, we will show how to compute the LC of an example. First, we tokenize all examples in the training set with NLTK word tokenizer 3 . Next, we rank unique words by word frequencies in descending order. Then, we compute the word ranks for all words in one example. After that, we calculate the third-quartile of log-ranks and use it as the LC for the example. Finally, we discretize all LC values into 40 discrete LC labels. LC label distribution is shown in Figure 2 (e). Therefore, we add 40 trainable LC control tokens (0-39) to the vocabulary. Since we utilize the BART tokenizer, feeding the original form of a targeted word into the encoder may hinder the decoder from injecting it into the output. As shown in Table 1, the token sequence for the targeted word banked' does not appear in the tokenized example (row 1). Therefore, the model must learn to map { b , anked } to { Gbank , ed } to include it in outputs, which undoubtedly increases the difficulty of incorporating the word into outputs. This problem is caused by the discrepancy between the token sequences of the targeted word and example. To solve this, we add an initial space to the targeted word and example so that the tokenized word appears in the tokenized example (row 2). By doing so, the decoder can copy the targeted word from the encoder to outputs instead of mapping, thus improving the word coverage by 8.6%. During training, we feed the targeted word, golden POS, Len and LC labels of examples into the en-612 coder, and then fine-tune the model by minimizing the cross-entropy loss. During inference, we set Len and LC to fixed values to generate examples with expected Len and LC. In text style transfer, Shen et al. (2017), Hu et al. (2017) and Li et al. (2018) used a pre-trained classifier to assess whether outputs have the desired attribute. Inspired by this, we propose a definition classifier to evaluate whether the targeted word w  in the example E conveys the given meaning D . The definition model takes a triple of word, definition and example ( w  , D, E ) as input. To train the definition model, we first create the synthetic data { ( w  , D, E, L ) } . If w  in E conveys D , the label L is 1, denoting the data instance is positive. Otherwise, L is 0, denoting the data instance is negative. We directly select the positive data instance ( w  , D, E ) from the Oxford training or validation set. Then, we create three kinds of negative data instances based on a positive data instance by (1) replacing w  with another word in E or vocabulary; (2) replacing D with another definition of w  or other words; (3) replacing E with another sentence, which does not contain w  . For ease of understanding, we show several synthetic data instances in Table 12 in the Appendix. We fine-tune BERT-base (Devlin et al., 2019) on the synthetic training set (see Figure 3 for the model input), achieving 89.9% F1 on the validation set. Similarly, we train a BERT-based POS classifier to assess whether w  in E reflects the given POS tag, which achieves 98.5% F1 on the synthetic validation set. We show the statistics of synthetic data for the definition and POS models, and their performance on the validation set in Appendix A and B. 4 Experiments 4.1 Experiment Setups Dataset and Pre-processing. We evaluate our proposed model on Oxford Dictionary 4 . Gadetsky et al. (2018) released a dataset based on this resource for definition generation. However, this dataset is unsuitable for dictionary example generation due to the following limitations (Chang et al., 2018): (1) each definition has only one example sentence; (2) some examples in their dataset do not contain targeted words. To solve these problems, we collect a new Oxford dataset by filtering out definitions with the number of examples less than two, and examples not containing the targeted word. In addition, we remove targeted words containing letters less than two or greater than 20. Each data instance is a quadruplet, containing a targeted word, POS tag, definition and examples of the word usage. We split the dataset into training, validation and test sets based on the triplets (lemma, POS, definition), which are mutually exclusive across three sets (see Table 2 for statistics of this dataset). Different from the training set, the validation/test set only contains polysemous words with at least two definitions, since it is more challenging to generate examples for polysemy. During training, each sense along with all corresponding example sentences will be used to update models. During inference, we will generate only one example sentence for each sense, but each lemma in English may have multiple inflections (For example, inflected forms of the verb bank' include banked', banking', etc). Given that we use BLEU to evaluate the generation quality, we only keep the word form with most example sentences for each definition tuple (lemma, POS, definition) in the validation/test set. For the sense (bank', Verb, heap (a substance) into a mass or mound') in the test set, two examples contain banked' and only one example contains banking', so we keep examples containing banked'. The data distributions of the training set are shown in Figure 2. See Appendix C for the data distributions of validation and test sets. Baselines. We first implement two retrieval baselines by randomly selecting examples containing 4 https://en.oxforddictionaries.com/ 613 # Models/Metrics Coverage  POSA  DefA  B-2  B-4  SB-4  D-2  D-4  AveLen Latency  Retrieval Models 1 One-Billion-Word 96.4% 82.9% 35.6% 12.5% 1.6% 18.7% 53.4% 76.6% 28.7 9.033 2 Training set 97.3% 84.0% 35.6% 17.3% 6.8% 18.2% 54.3% 77.1% 27.3 0.371 Lexically Constrained Models without Definitions 3 sep-B/F 100.0% 86.1% 32.6% 25.1% 4.7% 44.8% 29.3% 61.0% 18.2 0.964 4 asyn-B/F 100.0% 86.1% 32.3% 24.5% 4.5% 43.0% 30.1% 63.1% 19.6 0.931 5 GBS 100.0% 83.8% 33.4% 17.0% 2.5% 61.6% 23.7% 44.4% 19.9 7.854 6 X-MCMC-C 100.0% 0.1% 7.5% 15.6% 2.3% 15.1% 53.2% 95.0% 12.1 24.23 Lexically Constrained Models with Definitions 7 sep-B/F 100.0% 87.7% 77.1% 27.9% 6.4% 30.0% 43.5% 83.2% 15.5 1.002 8 asyn-B/F 100.0% 89.6% 77.5% 27.8% 6.2% 30.0% 42.9% 83.9% 16.9 0.991 9 GBS 100.0% 91.3% 77.5% 26.3% 6.1% 28.1% 44.6% 84.2% 15.9 8.025 Our Models + Word + POS + Len 14 + LC 25 10 Random (greedy) 99.8% 96.9% 81.8% 28.0% 5.4% 40.5% 34.3% 69.9% 14.0 0.164 11 BART-base (greedy) 99.6% 97.2% 87.7% 28.8% 7.6% 23.9% 49.5% 86.4% 14.0 0.161 12 BART-base (beam 5) 99.5% 97.4% 87.8% 31.4% 9.6% 26.2% 46.8% 84.2% 14.1 0.195 Table 3: Results on the Oxford test set. the targeted words from the One-Billion-Word 5 corpus or the training set, respectively. We adopt four lexically constrained generation models: two variants of the backward forward model (sep-B/F and asyn-B/F) (Mou et al., 2015), grid beam search (GBS) (Hokamp and Liu, 2017) and X-MCMC-C (He and Li, 2021). We implement the former three baselines based on GPT-2 small (117M). We train X-MCMC-C with the code provided by He and Li (2021), which is based on XLNet-base (110M). These methods generate sentences containing targeted words without considering definitions. To remedy this, we re-implement the former three models based on BART-base (139M), where the encoder takes the definition as input and the decoder incorporates the word during inference. Implementation Details. We initialize our model with BART-base, which has comparable parameters to generation baselines. For generation baselines and our models, we use AdamW (Loshchilov and Hutter, 2019) with an initial learning rate of 1 e  5 to update parameters for four epochs and choose the checkpoints with the lowest validation loss. During inference, we run beam search decoding with beam width = 5 on generation baselines and our model. We also run greedy decoding on our model. Following He and Li (2021), we run X-MCMC-C for 200 steps and select the example with the lowest negative log-likelihood (NLL) as output. To discourage the generation of repetitive tokens, we apply the repetition penalty strategy Keskar et al. (2019) with the penalized parameter = 1.3 to all models. We implement all models with the HuggingFace Transformers library (Wolf et al., 5 http://www.statmt.org/lm-benchmark/ 2019). Evaluation Metrics. We evaluate the generated examples from four aspects: Q1: Whether the generated example contains the targeted word? Q2: Whether the targeted word in the generated example conveys the given sense? Q3 & Q4: Whether the outputs are fluent and diverse? First, we check whether the targeted word appears in the example, indicated as word Coverage . If so, we will further assess whether the targeted word conveys the given POS tag and sense with the BERT-based POS and definition models, called POS Accuracy ( POSA ) and Definition Accuracy ( DefA ). As for Q3, it is non-trivial to evaluate the generation quality. In this paper, we do not use NLL as a metric for sentence fluency, since lower NLL does not always denote better sentence quality (Holtz-man et al., 2020). We use BLEU (Papineni et al., 2002) to measure the n-gram similarity between the generated examples and human references, which is a widely-used automatic metric for generation quality. One concern is that BLEU may be not ideal for dictionary example generation, since there may exist many sentences that could be appropriate for a given word and definition. To remedy this, each sense (i.e., definition triplet) in the validation and test sets contains an average of 11 examples, which provide a richer and more diverse test-bed for further automatic evaluation. To answer Q4, we use Self-BLEU (Zhu et al., 2018) and Distinct n-gram (Li et al., 2016) to measure the generation diversity. Self-BLEU-4 ( SB-4 ) is computed by treating one sentence as the hypothesis, and the first 1K generated sentences excluding the hypothesis as 614 references. Distinct bigram ( D-2 ) and 4-gram ( D-4 ) indicate the proportions of unique bigrams and 4-grams, respectively. Table 3 reports the main experiment results on the test set, from which we can draw four conclusions: (1) Generation models are critical. We cannot retrieve examples for all words. For example, only 97.3% of the words in the test set appear in the training set. We do not see any improvements with a larger dataset (rows 2), yet brings a much higher retrieval latency. By comparison, the generation models have the potential to generate examples for unseen words, thus greatly improving coverages. (2) The definition is helpful. Compared with generation baselines w/o definitions (rows 3-6), their counterparts w/ definitions (rows 7-9) significantly improve DefA to around 77%. As we have mentioned before, all words in the test set are polysemous (see Figure 6 (a)). That is why the definition is useful and indispensable for this task. (3) The pre-trained model does matter. Compared with the random counterpart (row 10), our model initialized with the BART-base model (row 11) can generate more fluent (B-4) and diverse (SB-4, D2, D4) sentences while improving DefA by around 6%. That is possibly because BART acquires some syntactic and semantic knowledge during pre-training, which is useful for this task. (4) The proposed models outperform other generation baselines in most metrics. One problem with lexically constrained generation models (rows 7-9) is that they do not explicitly explore the correlation between the targeted word and input. When feeding a definition into these models, they just generate a sentence based on the encoder input and force the targeted word to appear in outputs. By interfering with decoding, they can achieve 100% word coverage, yet this is achieved at the cost of generation quality, POSA and DefA. Another problem is that their manipulations of decoding cause higher inference latency. By comparison, our proposed model directly takes the targeted word as input instead of compulsorily injecting it into outputs during inference. This simple change brings two advantages over the lexically constrained generation methods: (1) Our model can fully explore the correlation between the targeted word and the definition, and gradually acquires this task during training. As a result, when # Variants Coverage  POSA  DefA  B-4  D-4  1 Full model 99.6% 97.2% 87.7% 7.6% 86.4% 2  Word 14.5% 17.3% 16.1% 3.6% 85.9% 3  POS 99.6% 96.6% 87.6% 7.5% 86.6% 4  Definition 99.4% 97.4% 35.8% 4.3% 73.2% Table 4: Results of ablation study on the test set. feeding a targeted word and a definition into the proposed model, it will understand that the reasonable outputs should contain the targeted word under the specific sense. That is why even the proposed model does not control the decoding process, it does not sacrifice the word coverage (e.g. 99.6% word coverage in row 11). (2) Eliminating interference to decoding brings substantial improvements in generation quality (B-4), POSA, and DefA, and dramatically reduces inference latency. We perform an ablation study to demonstrate the importance of each design. We first train variants of the full model by removing the word, POS, and definition, and then run greedy decoding on the well-trained models to generate examples. We show the results on the test set 6 in Table 4. Compared with the full model (row 1), we note that: (1) removing the targeted word significantly decreases the word coverage (row 2). (2) POS helps to improve the POSA (row 3). (3) the definition improves the DefA (row 4). These observations verify the effectiveness of these components. Len and LC control tokens are mainly used to control the readability of outputs, which do not degrade the generated examples (see Table 13 in the Appendix). We also test the effect of leading space. As shown in Table 5, adding the space increases the word coverage by 8.6%, establishing the importance of this design. The pointer network (Gul-cehre et al., 2016; See et al., 2017) is used to copy content from the source into outputs. However, only using the pointer network cannot improve the word coverage, as it does not solve the mapping issue mentioned in Section 3.3. Therefore, we do not use the pointer network. Effect of Control Tokens. In Table 13, we have shown that Len and LC control tokens affect readability via HF, but two questions are still unclear: Q1: Whether these control tokens have the desired effects on their associated attributes, length and lexical complexity? Q2: What is the correlation between LC and readability? To answer Q1, we generate examples by running greedy decoding on our model (row 11 of Table 3) with different control tokens. From Figure 4 (a) and (b), we see that: (1) the average LC and Len of outputs increase linearly with the gold LC and Len labels; (2) the MSE values between Len and gold Len labels are negligible, while the MSE values between LC and gold LC labels are relatively large, especially when LC > 30 indicating that the control ability of the model on LC decreases, possibly due to the limited training data (see Figure 2 (e)). Therefore, we can conclude that Len and LC control tokens do affect their associated attributes. widely used readability metrics, FKGL and FRES. Since LC is based on the word frequency, we compute the PCC between LC and the proportion of high-frequency words with a word rank lower than 2K (HF (2K)). FKGL and FRES are related to the average number of syllables of outputs (Ave-Syl), so we also compute PCC between LC and AveSyl. As shown in Figure 4 (c), LC values of outputs are strongly positively correlated with the gold/expected LC labels, which again verifies our model's control ability over LC. We also notice that PCC between LC and HF is -0.99, proving that LC can control the other readability-related metrics of outputs by controlling HF. We show more results of control tokens in Appendix H. Effect of the Number of Senses. As shown in the first part of Table 6, with the increase of the number of definitions, it becomes more and more challenging to generate examples satisfying the definition(s), thus causing a decrease in DefA. Effect of POS Tags. As shown in the second part of Table 6, our model performs worst on the adverb case, especially in POSA and DefA. We found that our model may ignore the adverb POS tag and use the adjective POS tag (see the targeted word  worse ' in Table 15). We presume that there are two possible reasons: (1) in the training set, the adverb training data is far less than the adjective data (see Figure 2 (c)), so the adverb embedding may not be well learned and updated; (2) for some adverb, such as worse' , the adjective definition is much more common, so the pre-trained model, BART, may bias towards the adjective meaning. words and the same word with different definitions, such as star' and satisfy' . Moreover, our model can generate plausible examples for different inflected forms of words, such as sentence' . Table 8 shows that we can control the length and lexical complexity of examples generated with our model by varying the control labels. To summarize, our model not only can generate meaningful examples for existing words, but also has a strong control ability over the length and lexical complexity of outputs. Please refer to Appendix E, F and G for the effect of word frequencies, unseen words and the size of training data. Please refer to Appendix I for more detailed sample analysis. We conduct a human evaluation to further compare our model with asyn-B/F and GBS (rows 8, 9 and 11 of Table 3). For each model, we randomly select 50 generated examples and invite three annotators 7 to label the sentences. Annotators first rate the sentence fluency on a 5-point Likert scale from 1 (not fluent) to 5 (extremely fluent). Then, annotators assess whether the meaning of the targeted word in the output is the same as the given definition on a 3-point Likert scale, from 1 (totally different) to 3 (exactly the same). Finally, annotators judge whether the POS of the targeted word in the output is consistent with the given POS. We show the detailed annotation method in Appendix D. As shown in Table 9, our proposed model outperforms baselines in human evaluation on all metrics. PCCs between two automatic evaluation metrics (DefA, POSA) and related human evaluation scores are 73.5% and 90.3% ( p -value < 0.05), indicating positive and strong correlations. We also conduct a human evaluation to assess our model's control ability over readability. We first randomly select 50 groups of examples generated by our model with different LC (10, 20, 7 All annotators are Ph.D. students and are independent of our research group. 617 30) + LC 14 . Then, we ask annotators to rank the sentences on readability in each group. The most difficult sentence receives a score of 3, the others receive scores of 2, 1. Annotators can give the same rank to different examples if they have no preference. As shown at the bottom of Table 9, the difficulty of the generated sentences increases with LC, verifying that our model can control the readability of outputs via LC control tokens. Inter-rater agreement measured by Fleiss' kappa (Fleiss, 1971) is 0.51, 0.73, 0.90 and 0.60 for fluency, definition, POS and readability, indicating moderate, substantial, almost perfect and moderate inter-rater agreement, according to Landis and Koch (1977). Word Sense Disambiguation. Word Sense Disambiguation (WSD) (Navigli, 2009) is a fundamental task and long-standing challenge in NLP, which aims to associate an ambiguous word in context with the exact sense from a finite set of possible choices. Previous work formulates the task as a token classification problem (Raganato et al., 2017) or sentence-pair (context and gloss pair) classification problem (Huang et al., 2019). WiC (Pilehvar and Camacho-Collados, 2019) is framed as a binary classification problem, which aims to identify if the occurrences of the targeted word in the first context and second context correspond to the same meaning or not. Our proposed work is related to word disambiguation, yet it is a generation task, which is more challenging. Controllable Text Generation. Controllable text generation aims to generate text in a controlled way, which has attracted wide attention. One line of research injects pre-specified keywords into outputs by controlling the decoding process (Mou et al., 2015; Hokamp and Liu, 2017; Post and Vilar, 2018) or refining candidate outputs iteratively (Miao et al., 2019; Sha, 2020; He and Li, 2021; He, 2021). Another kind of work uses control tokens to manipulate text attributes, such as the length (Kikuchi et al., 2016; Fan et al., 2018), topic (Ficler and Goldberg, 2017; Keskar et al., 2019), and grade level for text simplification (Scarton and Specia, 2018; Nishihara et al., 2019). In this paper, we first introduce the dictionary example generation task, which also requires the targeted word to appear in outputs. To this end, we use a target-word-aware model to generate examples for given words. Different from the former line of work, our proposed model does not interfere with the decoding process, thus reducing the inference time and improving the generation quality. Moreover, we expect to tailor-made outputs for different audiences. Inspired by the latter kind of work, our model takes readability-related control tokens to generate suitable example sentences with the desired readability. Dictionary Example Generation. Two recent works are related to dictionary example generation. One work is GPT-3 (Brown et al., 2020), a large-scale autoregressive language model. To qualitatively test GPT-3's ability for the few-shot task of using a new/nonexistent word in a sentence, Brown et al. (2020) gave GPT-3 the definition of a nonexistent word, such as screeg, and then asked GPT-3 to use it in a sentence. However, they did not formally define this task. Similar to our work, another concurrent work (Barba et al., 2021) also gives a formal statement of the dictionary example generation task. However, they did not evaluate the quality of generated examples directly. In their work, they aimed to improve WSD models by augmenting WDS datasets with the generated examples. Compared with their work, we directly evaluate whether the targeted work in the generated example reflects the given sense and POS tag with the proposed BERT-based classifiers. Our work also explores how to generate suitable examples for different targeted audiences. 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 BERT-based 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. We would like to thank the anonymous reviewers for their constructive and informative feedback. This project is supported by the funding from HKU-SCF FinTech Academy.
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