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  "evidence": ["In our experiments, we compare the performance of our model against BERT and RoBERTa."]
}

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Paper: A Neural Approach to Irony Generation

Ironies can not only express stronger emotions but also show a sense of humor. With the development of social media, ironies are widely used in public. Although many prior research studies have been conducted in irony detection, few studies focus on irony generation. The main challenges for irony generation are the lack of large-scale irony dataset and difficulties in modeling the ironic pattern. In this work, we first systematically define irony generation based on style transfer task. To address the lack of data, we make use of twitter and build a large-scale dataset. We also design a combination of rewards for reinforcement learning to control the generation of ironic sentences. Experimental results demonstrate the effectiveness of our model in terms of irony accuracy, sentiment preservation, and content preservation.

Introduction
The irony is a kind of figurative language, which is widely used on social media BIBREF0 . The irony is defined as a clash between the intended meaning of a sentence and its literal meaning BIBREF1 . As an important aspect of language, irony plays an essential role in sentiment analysis BIBREF2 , BIBREF0 and opinion mining BIBREF3 , BIBREF4 .
Although some previous studies focus on irony detection, little attention is paid to irony generation. As ironies can strengthen sentiments and express stronger emotions, we mainly focus on generating ironic sentences. Given a non-ironic sentence, we implement a neural network to transfer it to an ironic sentence and constrain the sentiment polarity of the two sentences to be the same. For example, the input is “I hate it when my plans get ruined" which is negative in sentiment polarity and the output should be ironic and negative in sentiment as well, such as “I like it when my plans get ruined". The speaker uses “like" to be ironic and express his or her negative sentiment. At the same time, our model can preserve contents which are irrelevant to sentiment polarity and irony. According to the categories mentioned in BIBREF5 , irony can be classified into 3 classes: verbal irony by means of a polarity contrast, the sentences containing expression whose polarity is inverted between the intended and the literal evaluation; other types of verbal irony, the sentences that show no polarity contrast between the literal and intended meaning but are still ironic; and situational irony, the sentences that describe situations that fail to meet some expectations. As ironies in the latter two categories are obscure and hard to understand, we decide to only focus on ironies in the first category in this work. For example, our work can be specifically described as: given a sentence “I hate to be ignored", we train our model to generate an ironic sentence such as “I love to be ignored". Although there is “love" in the generated sentence, the speaker still expresses his or her negative sentiment by irony. We also make some explorations in the transformation from ironic sentences to non-ironic sentences at the end of our work. Because of the lack of previous work and baselines on irony generation, we implement our model based on style transfer. Our work will not only provide the first large-scale irony dataset but also make our model as a benchmark for the irony generation.
Recently, unsupervised style transfer becomes a very popular topic. Many state-of-the-art studies try to solve the task with sequence-to-sequence (seq2seq) framework. There are three main ways to build up models. The first is to learn a latent style-independent content representation and generate sentences with the content representation and another style BIBREF6 , BIBREF7 . The second is to directly transfer sentences from one style to another under the control of classifiers and reinforcement learning BIBREF8 . The third is to remove style attribute words from the input sentence and combine the remaining content with new style attribute words BIBREF9 , BIBREF10 . The first method usually obtains better performances via adversarial training with discriminators. The style-independent content representation, nevertheless, is not easily obtained BIBREF11 , which results in poor performances. The second method is suitable for complex styles which are difficult to model and describe. The model can learn the deep semantic features by itself but sometimes the model is sensitive to parameters and hard to train. The third method succeeds to preserve content but cannot work for some complex styles such as democratic and republican. Sentences with those styles usually do not have specific style attribute words. Unfortunately, due to the lack of large irony dataset and difficulties of modeling ironies, there has been little work trying to generate ironies based on seq2seq framework as far as we know. Inspired by methods for style transfer, we decide to implement a specifically designed model based on unsupervised style transfer to explore irony generation.
In this paper, in order to address the lack of irony data, we first crawl over 2M tweets from twitter to build a dataset with 262,755 ironic and 112,330 non-ironic tweets. Then, due to the lack of parallel data, we propose a novel model to transfer non-ironic sentences to ironic sentences in an unsupervised way. As ironic style is hard to model and describe, we implement our model with the control of classifiers and reinforcement learning. Different from other studies in style transfer, the transformation from non-ironic to ironic sentences has to preserve sentiment polarity as mentioned above. Therefore, we not only design an irony reward to control the irony accuracy and implement denoising auto-encoder and back-translation to control content preservation but also design a sentiment reward to control sentiment preservation.
Experimental results demonstrate that our model achieves a high irony accuracy with well-preserved sentiment and content. The contributions of our work are as follows:

Our Dataset
In this section, we describe how we build our dataset with tweets. First, we crawl over 2M tweets from twitter using GetOldTweets-python. We crawl English tweets from 04/09/2012 to /12/18/2018. We first remove all re-tweets and use langdetect to remove all non-English sentences. Then, we remove hashtags attached at the end of the tweets because they are usually not parts of sentences and will confuse our language model. After that, we utilize Ekphrasis to process tweets. We remove URLs and restore remaining hashtags, elongated words, repeated words, and all-capitalized words. To simplify our dataset, We replace all “ INLINEFORM0 money INLINEFORM1 " and “ INLINEFORM2 time INLINEFORM3 " tokens with “ INLINEFORM4 number INLINEFORM5 " token when using Ekphrasis. And we delete sentences whose lengths are less than 10 or greater than 40. In order to restore abbreviations, we download an abbreviation dictionary from webopedia and restore abbreviations to normal words or phrases according to the dictionary. Finally, we remove sentences which have more than two rare words (appearing less than three times) in order to constrain the size of vocabulary. Finally, we get 662,530 sentences after pre-processing.
As neural networks are proved effective in irony detection, we decide to implement a neural classifier in order to classify the sentences into ironic and non-ironic sentences. However, the only high-quality irony dataset we can obtain is the dataset of Semeval-2018 Task 3 and the dataset is pretty small, which will cause overfitting to complex models. Therefore, we just implement a simple one-layer RNN with LSTM cell to classify pre-processed sentences into ironic sentences and non-ironic sentences because LSTM networks are widely used in irony detection. We train the model with the dataset of Semeval-2018 Task 3. After classification, we get 262,755 ironic sentences and 399,775 non-ironic sentences. According to our observation, not all non-ironic sentences are suitable to be transferred into ironic sentences. For example, “just hanging out . watching . is it monday yet" is hard to transfer because it does not have an explicit sentiment polarity. So we remove all interrogative sentences from the non-ironic sentences and only obtain the sentences which have words expressing strong sentiments. We evaluate the sentiment polarity of each word with TextBlob and we view those words with sentiment scores greater than 0.5 or less than -0.5 as words expressing strong sentiments. Finally, we build our irony dataset with 262,755 ironic sentences and 102,330 non-ironic sentences.
[t] Irony Generation Algorithm 
 INLINEFORM0 pre-train with auto-encoder Pre-train INLINEFORM1 , INLINEFORM2 with INLINEFORM3 using MLE based on Eq. EQREF16 Pre-train INLINEFORM4 , INLINEFORM5 with INLINEFORM6 using MLE based on Eq. EQREF17 INLINEFORM7 pre-train with back-translation Pre-train INLINEFORM8 , INLINEFORM9 , INLINEFORM10 , INLINEFORM11 with INLINEFORM12 using MLE based on Eq. EQREF19 Pre-train INLINEFORM13 , INLINEFORM14 , INLINEFORM15 , INLINEFORM16 with INLINEFORM17 using MLE based on Eq. EQREF20 
 INLINEFORM0 train with RL each epoch e = 1, 2, ..., INLINEFORM1 INLINEFORM2 train non-irony2irony with RL INLINEFORM3 in N INLINEFORM4 update INLINEFORM5 , INLINEFORM6 , using INLINEFORM7 based on Eq. EQREF29 INLINEFORM8 back-translation INLINEFORM9 INLINEFORM10 INLINEFORM11 update INLINEFORM12 , INLINEFORM13 , INLINEFORM14 , INLINEFORM15 using MLE based on Eq. EQREF19 INLINEFORM16 train irony2non-irony with RL INLINEFORM17 in I INLINEFORM18 update INLINEFORM19 , INLINEFORM20 , using INLINEFORM21 similar to Eq. EQREF29 INLINEFORM22 back-translation INLINEFORM23 INLINEFORM24 INLINEFORM25 update INLINEFORM26 , INLINEFORM27 , INLINEFORM28 , INLINEFORM29 using MLE based on Eq. EQREF20 

Our Method
Given two non-parallel corpora: non-ironic corpus N={ INLINEFORM0 , INLINEFORM1 , ..., INLINEFORM2 } and ironic corpus I={ INLINEFORM3 , INLINEFORM4 , ..., INLINEFORM5 }, the goal of our irony generation model is to generate an ironic sentence from a non-ironic sentence while preserving the content and sentiment polarity of the source input sentence. We implement an encoder-decoder framework where two encoders are utilized to encode ironic sentences and non-ironic sentences respectively and two decoders are utilized to decode ironic sentences and non-ironic sentences from latent representations respectively. In order to enforce a shared latent space, we share two layers on both the encoder side and the decoder side. Our model architecture is illustrated in Figure FIGREF13 . We denote irony encoder as INLINEFORM6 , irony decoder as INLINEFORM7 and non-irony encoder as INLINEFORM8 , non-irony decoder as INLINEFORM9 . Their parameters are INLINEFORM10 , INLINEFORM11 , INLINEFORM12 and INLINEFORM13 .
Our irony generation algorithm is shown in Algorithm SECREF3 . We first pre-train our model using denoising auto-encoder and back-translation to build up language models for both styles (section SECREF14 ). Then we implement reinforcement learning to train the model to transfer sentences from one style to another (section SECREF21 ). Meanwhile, to achieve content preservation, we utilize back-translation for one time in every INLINEFORM0 time steps.

Pretraining
In order to build up our language model and preserve the content, we apply the auto-encoder model. To prevent the model from simply copying the input sentence, we randomly add some noises in the input sentence. Specifically, for every word in the input sentence, there is 10% chance that we delete it, 10 % chance that we duplicate it, 10% chance that we swap it with the next word, or it remains unchanged. We first encode the input sentence INLINEFORM0 or INLINEFORM1 with respective encoder INLINEFORM2 or INLINEFORM3 to obtain its latent representation INLINEFORM4 or INLINEFORM5 and reconstruct the input sentence with the latent representation and respective decoder. So we can get the reconstruction loss for auto-encoder INLINEFORM6 : DISPLAYFORM0 DISPLAYFORM1 
In addition to denoising auto-encoder, we implement back-translation BIBREF19 to generate a pseudo-parallel corpus. Suppose our model takes non-ironic sentence INLINEFORM0 as input. We first encode INLINEFORM1 with INLINEFORM2 to obtain its latent representation INLINEFORM3 and decode the latent representation with INLINEFORM4 to get a transferred sentence INLINEFORM5 . Then we encode INLINEFORM6 with INLINEFORM7 and decode its latent representation with INLINEFORM8 to reconstruct the original input sentence INLINEFORM9 . Therefore, our reconstruction loss for back-translation INLINEFORM10 : DISPLAYFORM0 
And if our model takes ironic sentence INLINEFORM0 as input, we can get the reconstruction loss for back-translation as: DISPLAYFORM0 

Table 2: Automatic evaluation results of the transformation from non-ironic sentences to ironic sentences.*

Table 6: Automatic evaluation results of the transformation from ironic sentences to non-ironic sentences.

Table 8: Example outputs of the transformation from ironic sentences to non-ironic sentences. We use red and blue to annotate the clashes in the sentences.

Question: What are the difficulties in modelling the ironic pattern?
{"answer": ["obscure and hard to understand", " lack of previous work and baselines on irony generation"], "evidence": ["Although some previous studies focus on irony detection, little attention is paid to irony generation. As ironies can strengthen sentiments and express stronger emotions, we mainly focus on generating ironic sentences. Given a non-ironic sentence, we implement a neural network to transfer it to an ironic sentence and constrain the sentiment polarity of the two sentences to be the same. For example, the input is \u201cI hate it when my plans get ruined\" which is negative in sentiment polarity and the output should be ironic and negative in sentiment as well, such as \u201cI like it when my plans get ruined\". The speaker uses \u201clike\" to be ironic and express his or her negative sentiment. At the same time, our model can preserve contents which are irrelevant to sentiment polarity and irony. According to the categories mentioned in BIBREF5 , irony can be classified into 3 classes: verbal irony by means of a polarity contrast, the sentences containing expression whose polarity is inverted between the intended and the literal evaluation; other types of verbal irony, the sentences that show no polarity contrast between the literal and intended meaning but are still ironic; and situational irony, the sentences that describe situations that fail to meet some expectations. As ironies in the latter two categories are obscure and hard to understand, we decide to only focus on ironies in the first category in this work. For example, our work can be specifically described as: given a sentence \u201cI hate to be ignored\", we train our model to generate an ironic sentence such as \u201cI love to be ignored\". Although there is \u201clove\" in the generated sentence, the speaker still expresses his or her negative sentiment by irony. We also make some explorations in the transformation from ironic sentences to non-ironic sentences at the end of our work. Because of the lack of previous work and baselines on irony generation, we implement our model based on style transfer. Our work will not only provide the first large-scale irony dataset but also make our model as a benchmark for the irony generation."]}