Non-compositional Expression Generation Based on Curriculum Learning and Continual Learning
Authors: Jianing Zhou
Ziheng Zeng
Hongyu Gong
Suma Bhat
Venue: EMNLP 2023 Findings (2023)
Keywords: Non-compositional expression
Curriculum learning
continual learning

Abstract: Non-compositional expressions, by virtue of their non-compositionality, are a classic `pain in the neck' for NLP systems. Different from the general language modeling and generation tasks that are primarily compositional, generating non-compositional expressions is more challenging for current neural models, including large pre-trained language models. The main reasons are 1) their non-compositionality, and 2) the limited data resources. Therefore, to make the best use of available data for modeling non-compositionality, we propose a dynamic curriculum learning framework, which learns training examples from easy ones to harder ones thus optimizing the learning step by step but suffers from the forgetting problem. To alleviate the forgetting problem brought by the arrangement of training examples, we also apply a continual learning method into our curriculum learning framework. Our proposed method combined curriculum and continual learning, to gradually improve the model's performance on the task of non-compositional expression generation. Experiments on idiomatic expression generation and metaphor generation affirm the effectiveness of our proposed curriculum learning framework and the application of continual learning. Our codes are available at https://github.com/zhjjn/CL2Gen.git.

Non-compositional Expression Generation Based on Curriculum Learning and Continual Learning
Jianing Zhou', Ziheng Zeng', Hongyu Gong', Suma Bhat' !University of Illinois Urbana-Champaign
Faacbbook Al
(zjn1746, zzeng13, spphatt))eillnnneeeet fnygoggfb..com com
Abstract
idioms indexed by the Oxford Dictionary have a frequency of less than per million in the cor- pus of Contemporary American English (Rafat- bakhsh and Ahmadi, 2019). They have been clas- sically regarded as a "pain in the neck" to NLP systems (Sag et al., 2002) not only because of their non-composiiiionaliyyy but also because of their contextual semantic ambiguity (used in non- compositional or compositional meaning depend- ing on the context). Different NLP tasks related non-compositional expressions have been studied, including sentiment analysis (Biddle et al., 2020), paraphrase generation (Zhou et al., 2021c), natu- ral language inference (Chakrabarty et al., 2021a), metaphor detection (Su et al., 2020) and idiom us- age recognition (Liu and Hwa, 2018). However, the generation of non-composittioall expressions remains important yet under-explored problem. Therefore, this paper focuses on the generation of non-compositional expressions.
Non- cancoopposiiinall expressions, by virtue of their non-compositionaliyy. are classic 'pain in the neck' for NLP systems. Different from the general language modeling and generation tasks that are primarily compositional, gener- ating non-compositional expressions is more challenging for current neural models, includ- ing large pre-trained language models. The main reasons are 1) their non-compositionallty, and 2) the limited data resources. There- fore, make the best use of available data for modeling non-compositionaaliyy we pro- pose a dynamic curriculum learning frame- work, which learns training examples from easy ones to harder ones thus optimizing the learning step by step, but suffers from the for- getting problem. To alleviate the forgetting problem brought by the arrangement of train- ing examples, also apply continual learn- ing method into our curriculum learning frame- work. Our proposed method combined cur- riculum and continual learning, gradually improve the model's performance on the task of non composopoiii expression generation. Experiments idiomatic expression gener- ation and metaphor generation affirm the ef- fectiveness of our proposed curriculum leam- ing framework and the application of continual learning. Our codes are available at https: //gi f/github.... com/ Cnnjin/CI/GGrn git.
As shown in Table non-compositional expression generation aims to generate the cor- rect non-compositional expression given sen- tence with original non-compositional expression masked. Its importance stems from that 1) non- compositional expressions are an important part of everyday human language use, and 2) their use imparts naturalness, fluency and stylistic hancement. Therefore, the ability generate non-compositional expressions renders machine generated language more natural and human-like whereas current SOTA pre-trained text generation models only pre-trained on normal compositional language tend to only generate compositional ex- pressions (Zeng and Bhat, 2022). Besides, in our experiments, the simply fine-tuned model cannot correctly generate the idioms most of the time. Pre- viously only few of studies focus on metaphor generation (Yu and Wan, 2019; Chakrabarty et al., 2020; Stowe et al., 2021) whereas other types of non-compositional expressions remain under-
1 Introduction
Natural language has a common yet special class constructions called non-compositional expres- sions that exhibit semantic non-compositionalliy. where the meaning of the expression cannot be inferred from that of its constituent words (e.g., metaphors and idioms) (Baldwin and Kim, 2010). They are commonly used for specific communica- tive intents (Moon et al., 1998: Baldwin and Kim, 2010) and are individually rare but collectively fre- quent, appearing frequently across genres (Moon et al., 1998; Haagsma et al., 2020). Most of the

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Idiom
lem (French, 1993) ignored in previous curriculum learning works. Therefore, we propose RE-GEM, a continual learning algorithm to alleviate the for- getting of learned knowledge in the early stage. Overall, the main contributions are as follows:
Inpid Sentence
It looks like the temperature is going drop tonight. so be careful not to [MASK].
Output Sentence
It looks like the temperature is going to drop tonight,
so be careful not eatch cold.
Metaphor
Input Sentence
We conduct a first study on non-composiiioaal expression generation including both metaphors and idioms.
The scream [MASK] the night.
Outpud Sentence
The scream pierced the night.
Table Examples of input and output in our tasks. Non- compositional expressions are highlighted in bold red
We propose a novel curriculum learning framework specifically designed for non- compositional expression generation that uses the distance between contextualized represen- tations and word embeddings and perplexity score as a measure of difficulty level. is dynamically updated with the training, based on which the training examples are scheduled.
explored (c.g. idioms). The sparsity of literature and data resources presents challenges for the study of con-coopositional expression generation.
To better utilize available data and alleviate the limitation resources, curriculum learning (Ben- gio et al., 2009) aims enable the models to begin training from casier examples proceeding to ex- amples with an increasing level of difficulty. As such, curriculum learning consists of two core con- stituents: (1) Deciding the level of learning dif-
We point out for the first time the forgetting problem caused by the curriculum learning and propose a scheme RE-GEM to allevi- ate this problem.
ficulty for each example, and (2) Scheduling the order of training examples based on that difficulty level. Curriculum learning has recently emerged promising direction for different fields includ- ing computer vision (Weinshall al., 2018; Wang al., 2019; Li et al., 2020) and natural language processing (Platanios et al., 2019; Liu et al., 2020; Zhou et al., 2021d; Zhang et al., 2021). How- ever, despite the relative success on computer vi- sion tasks, the upplication of curriculum learning for natural language processing is still limited to neural machine translation, which has rich data resources while other applications, such as non- compositional expression generation, with limited data remain under-explored.
We evaluate proposed framework two tasks: idiomatic expression generation and metaphor generation. Experimental results on both tasks affirm the effectiveness of our framework. Detailed ablation studies and anal- ysis are provided to support our claims.
Related Work
Non-compositional Expression. As an integral part of natural language, non-compositional ex- pressions are classically regarded as a "pain in the neck" for NLP (Sag et al., 2002) due to their non- compositionality. Prior studies mainly focused on tasks related to non-compositional expressions, in- cluding identifying potentially idiomatic expres- sions (Salehi et al., 2014; Senaldi et al., 2016; Flor and Klebanov, 2018; Amin et al., 2021: Zeng and Bhat, 2021), disambiguating between their figura- tive/literal use (Peng and Feldman, 2015; Köper and im Walde, 2016; Liu and Hwa, 2017, 2018). detecting metaphors (Gao et al., 2018; 8; Mao et al., 2019; Su et al., 2020; Gong et al., 2020), generating metaphors (Yu and Wan, 2019; Stowe et al., 2020; Chakrabarty et al., 2020; Stowe et al., 2021) and paraphrasing between non-compositionall expres- sions and their literal counterparts (Liu and Hwa, 2016; Agrawal et al., 2018; Shirin and Raseek. 2018: Zhou et al., 2021a,b). However, owing their con-composttionality and limitations on avail- able and related data resources (Stowe et al., 2020,
To this end, we propose novel curriculum learning framework for non-compositional expres- sion generation to fill the research gap of generat- ing non- non-compositnee conposoppsiii expressions including both metaphors and idioms. Our study is the first to focus on this task and utilizes curriculum learning to alleviate the problem caused by limited data re- sources. In our work, we use the representation distance and the perplexity score as the difficulty measurement and a dynamic scheduling method order the examples. Specifically, we observe that curriculum learning orders the training exam- ples according to difficulty level to create a gradual shift of distribution of domain difficulty, which will forgeetnniic cause the well- known catastrophic forgetting prob-

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2021), the task of generating non-compositional expressions including both idioms and metaphors remains challenging and under-explored. Our study first focuses on alleviating the limitation on re- source availability concerning the generation of both idioms and metaphors.
able studies on curriculum learning mentioning this forgetting problem. Our work is the first attempt to point out the issue of forgetting in curriculum learning and study mechanisms to alleviate it.
Framework
Curriculum Learning, First proposed by (Bengio et al., 2009), curriculum learning enables machine learning model training to gradually proceed from easy examples to harder ones according to mea- sure of difficulty level for each example, thereby permitting better utilization of available data re- sources. With growing research interests received, curriculum learning has been applied to different fields including computer vision (Weinshall et al., 2018; Wang et al., 2019; Li et al., 2020) and natural language processing. Despite its benefits observed in computer vision tasks, including image classi- fication (Weinshall et al., 2018), human attribute analysis (Wang al., 2019) and visual question answering (Li et al., 2020), has seen limited ap- plicability in NL.P mainly NMT (Platanios et al., 2019; Liu et al., 2020; Zhou et al., 2021d). As a result, curriculum learning methods, including difficulty measurement and scheduling strategies, are mainly designed for the NMT task, which is largely different from the task of processing non- compositionality (non-compositional expression generation). To this end, we propose our curricu- lum learning method specifically designed for non- compositional expression generation.
In this section, we briefly introduce our proposed curriculum learning method for non-compositional expression generation.
Curriculum learning for efficiently leveraging available data resources consists of two main parts: measure of difficulty of training instances, and an arrangement of the training examples using this measure. Accordingly, for non-compositional ex- pression generation, we propose data arrange- ment method for dynamically arranging the train- ing examples according to a newly studied diffi- culty metric. In addition, due to the current large pre-trained language models' insufficiency in pro- cessing non-composittioall expressions (Dankers et al., 2022), non-compositional expressions that are difficulty for LMs understand would have a high perplexity score and the representations be- tween non-compositional expressions and their con- stituent words would be large. Therefore, we use a combination of the representation distance and per- plexity score as measure of examples' difficulty. Moreover, in experiments, observe that following the curriculum lcarning principle of ar- ranging the training examples based on their diffi- culty levels, the problem of forgetting arises due to the gradual shift of distribution in domain difficulty. Therefore, to alleviate this forgetting problem, we propose a simple yet effective continual learning method. Figure 1 demonstrates the workflow of our proposed curriculum learning framework and its details follows.
Continual Learning Continual learning enables models to learn new knowledge and preserve knowledge acquired previously from a data stream with a continuously changing distribution. How- ever, due to the well-known problem of catas- trophic forgetting (French, 1993), continual learn- ing is still challenging for current neural models. The same forgetting problem could also appear in curriculum learning because curriculum learning re- arranges the examples according to their difficulty levels, which will naturally create a training data stream with continuously changing distribution on difficulty domain and thus cause the problem of forgetting. Although the catastrophic forgetting problem has been explored in both computer vi- sion (Rebuffi al., 2017; Kirkpatrick et al., 2017; Zenke et al., 2017; Aljundi et al.. 2019) and natural language processing (Xu et al., 2018; Liu al., 2019; Sun et al., 2019; Chen et al., 2015; Shu et al., 2016; Thompson et al., 2019), there are no avail-
3.1 Difficulty Metrics
In this section, we define the difficulty metric used by our framework. Previous works on curricu- lum learning mainly focus on compositional lan- guages. Therefore, difficulty metrics, including sentence length, word rarity, embedding norm and etc, proposed in prior works cannot reflect the dif- ficulty levels of non-compositional expressions in the sentences. As mentioned in Section due to the non-coompsittonaaliyy the meaning of the non-compositional expressions is different from the meaning of the constituent words. Therefore, the distance between the ideal representation of the

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Memory
Ours
Continual
Ranking by Difficulty
Batch Sempled by Difficulty
Model
Dataset
Difficulty Update
Figure The overview of our framework and the comparison between our framework and other CL methods.
non-compositional expressions and the represen- tations of the constituent words would be large. Utilizing this property, we first propose to use the distance between the contextualized represen- tations of the non-compositional expressions and the original word embeddings of the constituent words to reflect the difficulty. A larger distance represents the model has already learned the rep- resentation of the expression instead of using the constituent words' embeddings, which means this non-compositional expression is easy for the model. On the contrary, a smaller distance means the target non-compositional expression is difficulty for the model. Therefore, the difficulty metric based on representation distance is calculated as follows:
that the language model more familiar with this expression, i.c., this expression is easier for the language model and thus is more likely to generate it. The difficulty metric based on perplexity score is calculated as follows:
dY PPL(Y; 0) = 1
>1 logno brees)
where the target sentence in a training example and 0 is the trainable parameters.
3.2 Scheduling Strategy
Having ascertained the difficulty level for each training example, the traditional curriculum learn- ing scheme re-arranges the training examples using the difficulty level and fixes the order of the exam- ples for the subsequent training process. However, after training the model some training examples, is expected that the perceived difficulty level of each training example will change. Therefore, it is unreasonable to use the same order of training examples for the entire training process. To address this issue, a competence score is proposed to dy- namically reflect the model's 's ability. However, the used by previous works, such number increased with time steps (Platanios al., 2019), is actually not comparable the difficulty score training examples because the two measure unrelated aspects. To better reflect the dynamic dif- ficulty levels, we propose a dynamic scheduling method that arranges the training examples.
1
d'(Y) DIST(Y; (((Y) - Emb(Y)
| where ((-) is the final layer of the model and Emb(-) is the embedding layer of the model.
Besides, mentioned in Section 1, due to the rareness of non-compositional expressions in large scale corpora relative to compositional expressions, large pre-trained models seldom see the use of non-compositional expressions during pre-training which results in their inability to accurately capture the semantics of these expressions. Therefore, we assess the difficulty of training examples based on the models' familiarity with the compooopoiiii expressions. Toward this, we propose utilize the perplexity score as measure of the difficulty of each training example. This stems from the idea that language modeling, perplexity is used as a quality measure for language models, which is indicative of its ability to predict the next word in sequence of words. Perplexity score is built with n- grams that are extracted from text corpora: lower perplexity score of n-gram X, i.e.,
competence
After each training epoch, the difficulty score d for each training example is updated using the model trained in the most recent epoch:
da = -'
d' + +
where d, (Y) is the difficulty score for training ex- ample (X; after the model has been fine-tuned for epochs and 0,, refers to the trainable param- eters of the model that has been fine-tuned for epochs. X represents the input sentence with the target non-compositional expression masked in the training example and Y is the target sentence. Af- ter the difficulty scores for all the training examples
(X) e > logre(a, (mex))
means the language model assigns higher proba- bility to generating X. Therefore, a lower perplex- ity on a non-compositional expression is indicative

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Algorithm 1: PPLCL
those with higher difficulty in each epoch. In this process, some knowledge about the examples with a lower difficulty level will be forgotten.
Input: Dataset Model M and number of epochs N
Output: Fine-tuned Model M"
Du [TP, M)
To alleviate the forgetting problem, we propose RE-GEM, a modified version of GEM (Lopez-Paz and Ranzato, 2017), which fits in our framework better compared with the traditional continual learn- ing methods for the following reason.
Sort P based each difficulty level Do. resulting
in re-arranged Po;
for 1; < N do
Ma,, + TRAIN(..-I):
D. P, ;
for (X:Y)
dy = d' (Y)+ (Y):
The traditional continual learning methods like GEM aim to alleviate the forgetting problem and thus sacrifice the learning ability on new data and part of the overall performance. This is done with the use of an episodic memory, Ma,, containing randomly sampled training examples from the data PP for the time step k. When minimizing the loss on the current time step I. GEM treats the losses on Mr of step k < t constraints by preventing their increase. An improved version of GEM (Chaudhry et al., 2018) was proposed only treat the loss on subset Mrrf of all the episodic memories for step k < t as constraint instead of computing multiple losses. To guarantee the loss reduction on this episodic memory subset, Mreff, their imple- mentation first computes the loss gradient vector g on the current step and then computes the loss gra- dient vector Gref on the subset Mref- Whenever the angle between g and 9ref greater than 90", the gradient g will be projected to 9 as:
(Y) ()) then
D, Dr (HH
10
P = U
11
clse
12
continue; :
13
end
end
Sort P, based on D... resulting in P..
16
17
return M M
Algorithm 2: TRAIN
Input: Train Dataset P. Model Ma Output: Fine-tuned Model M
M <
0:
for t
1:t S V do
for (X, Y) E P. do
Mree (Xref. Yrer) M:
(XX) Y);
Gref Val(Mo( (a(Ma((Xnn) (X (Xref), Yrel):
if Oret 2 then
0 + 0 ag:
else
Stge 5 Gres
a g: t A 00aag0000 +)):
end
1 1 9 s.t. gret 2
minimize 2
l12
12
[M]
13
and the parameters will be updated based on J. with the intent of avoiding the forgetting of previous data and learning from new data.
14
for i 1:1 < do
15
X. Y) v M M <- (X
16
end
17
end
Instead, our main focus is to learn from new data and then to alleviate the forgetting of previous data through the use of RE- GEM. Therefore, when the angle between g and Gref is greater than 90%, we first project gref to ğ as follows:
return M Ma;
have been updated, the training examples will be re-arranged according the new difficulty scores.
1 || Gref 9 S.L.
3.3 Continual Learning
minimizeg 2
g 2 (1)
Essentially, after the order of the training exam- plcs is re-arranged based on difficulty level, the curriculum learning scheme creates a gradual shift of the distribution in the domain difficulty, which will cause the problem of catastrophic forgetet currently ignored by previous studies of curricu- lum learning (Bengio et al., 2009; Weinshall et al., 2018; Wang al., 2019; Li et al., 2020; Platanios et al., 2019; 9; Liu et al., 2020; Zhou al., 2021d). During training, the model will first learn the ex- amples with a lower difficulty level and then learn
Then the parameters will be updated based on both and where guarantees the successful learning of current new data and tries best to alleviate the forgetting of previous data. We leave exploring other forms of gradient updates to future work.
The constraint optimization problem in Eq..1 can be solved via the rule proposed in (Chaudhry al., 2018) as follows:
I g.
9 gref

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Epoch
Epochs
Methods
Aec
BLEU
phrase-BI.El BL.UU
Rouge
phrase- phrase-Rouge
Acc
BLRU
phrase-BI.Re
Rouge
phrase- Mhrase-Rongee
Vanilla
56.65
37.74
69.94
40.69
25
61.87
43.82
73.27
48.81
Competence SL
53.82
35.02
67.90
37.81
24
61.11
43.42
72.62
47.77
Competence WR
54.01
35.87
68.09
38.11
24
61.16
44.18
72.91
47.56
Norm-based
54.12
35.43
67.96
38.02
24
61.34
44,22
73.06
48.04
Fixed SGCL
56,25
37.48
69.93
41,83
24
60.11
43.24
72.35
47.63
Dynamic SGCL
54.56
37.23
68.72
38.92
22
59.37
41,07
72.08
46.09
Ours
23
60.43
43.72
72.31
45.77
28
64.36
48.02
74,94
51,82
p-vglue
0.02
0.01
0.008
0.007
0.006
0.01
0.005
0.005
0.01
0.006
Table 2: Performance of different methods on MAGPIE dataset. Competence represents using competence score for scheduling. SL refers to using sentence length as difficulty score. WR refers to using word rarity as difficulty score. Best performance is labeled bold. Models trained for 5 epochs are converged. p- p-value refers to the results of significance test based our method and second best method (Fixed SGCL).
4 Experiments
ROUGE (Lin, 2004) and BLEU (Papineni et al., 2002) for evaluation. To automatically evaluate how well the idiomatic expressions are generated, we extract the newly generated part in the output sentences and then compare it with the target non- compositional expressions in the references via phrase-level BLEU and ROUGE scores following (Zhou et al., 2021c). We also evaluate with stricter metric of phrase-level Accuracy, in which the gen- erated non non-composiin concoopooiiio expression is considered to be correct if and only if every word strictly matches the target expression. For metaphoric ex- pression generation task, corpus-level BLEU score and ROUGE score are used for evaluation follow- ing (Chakrabarty et al., 2021b). Due to the fact that the target metaphoric expressions only contain word and is measured by ROUGE-1 score, we did not use the phrase-level scores described above. Human Evaluation We used 100 instances from test sets for both tasks, and collected the outputs from the 3 best methods ranked by automatic evalu- ation. For each output sentence, two native English speakers, who were blind the systems being com- pared, were asked to rate the output sentences. For idiom generation, we propose the following crite- ria: (1) Meaning ("Are the output and the reference meaning the same thing?") (2) Fitness ("Does the generated idiom make sense in the context?") (3) Fluency ("How fluent, grammatical, well formed and easy understand are the generated utter- ances?") (4) Overall ("What the overall quality of the generated utterances?"). For metaphor gen- eration, criteria described in (Chakrabarty et al., 2021b) is used. More details are the Appendix.
4.1 Datasets
We datasets focusing on kinds of non-compositional expressions-MAre (Haagsma et al., 2020) for idiom and MERMAID (Chakrabarly et al., 2021b) for metaphor. For the instances from MAGPIE, we mask the target idiom in each example. The position of the target idiom is provided in the dataset. The official suining-devllopment-tssiiii splits used. For MERMAID, the masked sentences have been provided and we use the available data splits.
4.2 Baselines
We tested six baseline models compare them with our proposed curriculum learning framework. The Vanilla model that does not use any CL meth- ods, Competence-based CL (Platanios et al., 2019). Norm-based CL (Liu et al., 2020) and SGCL (Zhou al., 2021d) are used as baselines. Due to space limitation, their description and experimental set- tings are provided in the Appendix.
4.3 Experimental Settings
For our framework, we utilize BART-base our backbone model. For the task of idiomatic expres- sion generation, the model is trained with batch size of 8 for epochs. For metaphor generation, the model is trained with batch size of 16 for 10 epochs. Adam optimizer used and the learning rate is set to 5 x 13- All the other parameters are set to their default. All of our experiments are performed 5 times and the mean of the results are reported. Beam search is used when decoding.
5 Results
4.4 Evaluation Metrics
Automatic Evaluation Considering our focus non-compositional expression generation, we use the widely used text generation evaluation metrics
shown in Table 2, for the idiomatic expression generation task, our proposed framework achieves the best performance with respect to all the evalu-

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Epoch
Epochs
BLEU
BLEU-2
BLEL-4
Rouge-1
Rouge-2
Rouge-L
BLEU
BLEU-2
BLEU-4
Kouge-1
Kouge-2
Rouge-1.
50.53
78.13
63.51
78.12
53.96
67.21
14.80
79.42
66.06
79.41
Competence SL.
49.55
63.88
39.04
77.80
62.82
77.78
50.21
64.21
39.69
78.02
63.17
78.01
Competence WR
49.19
63.46
38.59
77.61
62.55
77.59
53.76
65.78
42.90
78.81
65.00
78,80
Norm-hased
49.02
63.23
38.44
77.42
62.35
77443
53.54
65.55
42
78.60
64.88
78.65
Fixed
78.43
54.14
67.55
44.88
79.60
66.88
Dynamic SGCL
49.3
63.54
39.42
77.63
63.11
78.03
53,65
66.27
43.66
79.13
65.83
78,95
Ours
51.61
65.46
42.12
78.58
64.42
78.57
57.94
69.88
46.77
81.34
68.49
81.54
P-valune
0.003
0.006
0.002
0.003
0.004
0.007
0.006
0.007
0.003
0.006
0.007
0.008
Table 3: Performance of different methods on MERMAID dataset. Best performance is labeled in bold.
Epoch
Epochs
Methods
Acc
BLEU
MBEase-BLEU
Rouge
phrase- Chrase-Rouge
Acc
BLEU
phrase-BLEU
Rouge
phrase-Rouge
Vanilla
18
56.65
37.74
69.94
40.09
25
61.87
43.82
73.27
48.81
Diff + Fixed
57.36
38.10
70.22
42.52
25
62.05
45.20
73.40
49.40
Competence
58.33
39.12
42.73
62.44
SL. Competence
16
53.82
35.02
67.90
37.81
24
61.11
43.42
72.62
47.77
WR Competence
16
54.01
35.87
68.09
38.11
24
61.16
44.18
72.91
47.56
Norm Competence
16
$4.12
35.43
67.96
38.02
24
61.34
44.22
73.06
48.04
Diff+ + Dynamic
20
59.06
40.08
71.31
43.42
26
63.20
46.84
74.57
50.17
Diff + Dynamic Re-GEM
60.43
43.72
72.31
45.77
28
64.36
48.02
74.94
Dynamic ER
53.53
37.55
Diff Dynamnic MIR
59.06
40.48
71,58
43.71
25
63.32
45.55
74.39
49,86
Diff Dynamic AGEM
21
58.66
40.61
71.28
43.01
25
62.36
45.00
73.44
49.27
Table 4: Ablation study MAGPIE dataset. Diff' refers difficulty metric. Fixed means the training examples are sorted only once before training and fixed during training. Dynamic refers to our dynamic scheduling strategy.
ation metrics. Compared with the performance of the vanilla model, our framework outperforms it by 5 on accuracy, 3.78 BLEU, 5.98 phrase-level BLEU, 2.37 on Rouge and 5.08 on phrase-level ROUGE score after only training epoch. After 5 training epochs, the improvements after conver- gence are 3 on accuracy, 2.49 on BLEU, 4.2 on phrase-level BLEU, 1.67 on ROUGE and 3.01 on phrase-level ROUGE.
task, all the baseline curriculum learning methods' influence the performance is similar to that in the idiomatic expression generation task. That is, most of the bascline methods do are not competi- tive compared with the vanilla model, whereas our proposed curriculum learning framework shows an obvious improvement over the vanilla model.
Based on the performance on both tasks, we see that the baseline curriculum learning methods cannot effectively improve the performance of the vanilla model (and may even negatively influence the performance). However, our proposed curricu- lum learning framework outperforms all the base- line models by reasonably large margins.
For other baseline models, it is obvious that most of them are not competitive compared with the vanilla model. Some of the baseline methods even degrade the performance of the vanilla model.
Table 3 presents the results on the task of metaphor generation task. As in the case of id- iomatic expression generation, in this task pro- posed framework achieves the best performance considering all the evaluation metrics. Compared with the performance of vanilla model, our frame- work outperforms it by 1.08 on BLEU, 0.92 on BLEU-2, 1.71 on BLEU-4, 0.45 on ROUGE-1, 0.91 on ROUGE-2 and 0.45 on ROUGE-L after only 1 training epoch. After 10 training epochs, the improvements increase 3.98 on BLEU, 2.67 on BLEU-2, 1.97 on BLEU-4, 2.12 ROUGE-1, 2.43 on ROUGE-2 and 2.13 on ROUGE-L. Ta- ble presents the results human evaluation. shown that our method still outperforms other base- lines and vanilla model by large margin on both idiom and metaphor generation task.
6 Analysis
Here we provide some ablation studies based on id- iomatic expression generation to analyze the contri- bution of different modules used in framework. Difficulty measurement. As shown in Table 8, us- ing our difficulty metric can boost the performance of the vanilla model, which verifies the effective- ness of our proposed measurement of difficulty. Compared with the vanilla model, using our diffi- cuity metric alone can improve the performance by 2 accuracy, 0.71 on BLEU and 0.28 ROUGE even without the scheduling method. In addition, compared with the other difficulty measurement methods used in previous studies (e.g. sentence length, word rarity and norm), ours shows a larger performance improvement (rows 3-6 in Table 8).
should be noted that for metaphor generation

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Idiom
Metaphor
Model
Meaning
Fitness
Fluency
Overall
Fluency
Meaning
Creativity
Metaphoricity
Vanilla
1.88
1.58
3.97
2,48
3.51
3.32
3.3
3.08
Fixed SGCL
1.86
1.68
3.95
2.50
3.29
2.92
3.51
3,46
Ours
2.98
3.69
4.16
3.61
4.36
3.84
3.43
3.85
Table 5: Human evaluation results.
The best performance is in bold.
train, Joss, verilla
train Joss, pol
trein Joss, agem
train loss.reger regar
0.885
1.3
0.199
0.975
0.088
0.525
350 1074 1790 2506 3222 3585 4296 5012 5726 6644 7180 7518 5234 8950
281 1405
2528
3372 4496 9620 8463 7587 B438 9554 10578 115E1 12645
Figure 2: X-axis represents the training steps and Y-axis represents the training loss. (a) and (b) represents the results on MAGPIE dataset and MERMAID dataset respectively. 'vanilla" refers to the training loss of vanilla model. 'ppl', 'agem' and 'regem' refer to the training loss of the model using our CL methods without continual learning, with AGEM and our RE-GEM GEM.
Using our difficulty metric can thus outperform the best among sentence length, word rarity and norm by 4 on accuracy, 4.21 on BLEU and 2.34 on ROUGE, demonstrating its superiority for non- compositional expression generation.
8 in Table 8, the application of RE-GEM success- fully improved the performance by 3 on accuracy, 1.37 on BLEU, 3.64 on phrase-level BLEU, 1 on ROUGE and 2.35 on phrase-level ROUGE scores when only perplexity score and dynamic schedul- ing are used. Besides, rows 8-11 show the advan- tage of our proposed RE-GEM over other continual learning methods, including ER (Robins, 1995). MIR (Aljundi et al., 2019) and AGEM (Chaudhry et al., 2018) when applied to curriculum learning, The performance of RE-GEM is better than the best among ER, MIR and AGEM by on accuracy, 3.24 phrase-level BLEU and 2.06 phrase- level ROUGE, trend persisting in Figure 2. When the training loss using curriculum learning with continual learning suddenly spikes, the peak of the one using RE- GEM is the lowest. This suggests our RE-GEM more effective in alleviating the forget- ting problem while maintaining the performance in curriculum learning, especially compared with the traditional continual learning methods.
Dynamic scheduling strategy. The effectiveness our dynamic scheduling strategy can be verified by comparing rows and in Table 8. We see that using our difficulty metric, the performance improves by 1.7 BLEU and 1.09 ROUGE points via dynamic scheduling compared with the perfor- mance when fixed scheduling is used, which con- firms the effectiveness of our proposed dynamic scheduling method for curriculum learning.
Continual learning scheme. As stated in Sec- tion 3.3, curriculum learning will cause the forget- ting problem, which has been ignored by previous studies. As shown in Figure 2, compared with the training loss of the vanilla model, the training loss of the model using only curriculum learning without continual learning shows sudden peaks the beginning of each epoch. This shows that the model tends to forget knowledge learned from ear- lier (casier) examples in each epoch after curricu- lum learning is applied. Additionally, when the model moves from easier examples to harder exam- ples, the knowledge learned from easier examples is beneficial for learning harder ones resulting in non-increase of the training loss when the model learns harder examples during each epoch.
Conclusion and Future Work
In this paper, we first utilize curriculum learning to better utilize available data for non-compositional expression generation. We propose novel curricu- lum learning framework by utilizing representation distance and perplexity score mcasure of dif- ficulty level and dynamic scheduling method better leverage the available training data. Further- more, for the first time we study a continual learn- ing algorithm to alleviate the forgetting problem re- sulting from curriculum learning. Experiments on
To alleviate the forgetting problem, we proposed a continual learning algorithm called RE-GEN to help with the learning. As shown in rows and

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two non-compositional expression generation tasks of idiomatic expression generation and metaphor generation show that the proposed curriculum learn- ing framework can effectively boost the perfor- mance of non-compositional expression generation outperforming previously studied curriculum learn- ing methods. Future works should explore other difficulty metrics, more effective scheduling meth- ods and continual learning schemes to further al- leviate the forgetting problem, and study them for other text generation problems.
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A
Baseline Models
D.1 Idiom Generation
Given reference sentences, annotators are expected to evaluate the quality of the generated sentences from three aspects:
Details about the baseline models:
The vanilla model directly use the pre-trained BART model for fine-tuning. For each training batch and epoch, random sam- pling is used to select training examples. No curriculum learning methods are applied.
Vanilla:
1. Meaning: Are the output and the reference meaning the same thing? If yes, the score should be 3. If they are similar but not exactly the same, the score should be 2. If they are not similar, the score should be 1.
Competence-based CL: (Platanios et al., 2019) proposed to select training examples based on a competence score. Training exam- ples with a difficulty score lower than current competence score will be selected as candi- dates for training. In their study, they pro- posed two measures of difficulty score: sen- tence length and word rarity.
2. Fitness: Does the generated idiom make sense in the context? If the generated idiom always makes sense, the score should be 4. If the generated idiom makes sense under some cir- cumstances, the score should be 3. If the gen- crated idiom only makes sense under extreme circumstances, the score should be 2. If the generated idiom is invalid, the score should be 1.
(Liu et al., 2020) proposed to use norm of word embeddings obtained from neural networks as measure of both difficulty score and competence score. The selection of training examples similar with the procedure described above in competence- based CL..
Norm-based CL:
3. Fluency: check whether the transferred sen- tence is fluent and readable on scale of to 5, ranging from "highly non-fluent" to "very fluent". This should include the tense (present past), number(singular or plural) and pro- noun(himself,, herself, someone, his, her etc.)
SGCL: (Zhou et al., 2021d) proposed to uti- lize sentence-level BLEU score as measure of the learning difficulty level. For the ar- rangement of training examples, the original training set will be divided into several mutual exclusive subsets according to difficulty level. Then they proposed two ways of scheduling: fixed scheduling and dynamic scheduling. We use both scheduling methods as our baseline models. More details about this baseline are described in (Zhou et al., 2021d).
D.2 Metaphor Generation
Given input sentences and the reference sentences, annotators expected to evaluate the quality of the generated sentences from four aspects. A scale of 1-5 where denotes the worst and 5 be the best used:
Fluency: How fluent, grammatical, well formed and easy understand are the gen- erated utterances?
2. Mcaning:
Are the input and the output refer- ring meaning the same thing?
B Implementation
3. Creativity: How creative are the generated utterances?
Our experiments and implementation are based on the Transformers library and PyTorch.
Metaphoricity: How metaphoric are the gen- erated utterances
Experimental Details
All our experiments are conducted with 2 NVIDIA V100 GPUs.
D.3 Number of Parameters
Considering that our proposed curriculum learn- ing and continual learning do not introduce more parameters, the number parameters identical to the number of parameters in the underlying lan- guage model: 140M for BART(base).
D Human Evaluation
Here we provide more details of the human evalua- tion.

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Egoch
5 Epochs
Methods
BLEU
phrase-BLEU
Rouge
phrase- Rouge
Acc
BLEC
phrass-BLEY
Rouge
phrase- Rouge
YuaillaBARKT-arg BART-large
58.43
40.24
71.88
42.27
27
62.73
45.02
74.98
50.33
INIT Dynamic HERR--arge
60.47
42.85
74.02
45.21
04.55
47.83
77.02
53.11
Re-GEM -BRTTaagge
Vanilla-
56.65
Diff Dynumic -usee
20
59.06
40.08
71.31
43.42
26
03.20
46.84
74,57
50,17
Dilf Dynamic Re-GEM BART-base
60.43
43.72
72.31
45.77
64.36
48.02
74.94
51.82
Vanilla: 1S-large
$6.21
37.13
68.74
39.23
24
60.12
42.98
72.74
48.02
Dif Dynumic -T-durge
57.94
39.85
70.03
41,73
25
61.03
45.02
74,38
90,12
1Mf + Dynamic Re-GEM -T5-orge
69.74
42.25
71.94
48.72
27
62.91
47.03
76.58
51,82
Vanilla-T'S- TS-base
15
53.14
34.02
65.52
36.01
20
$6.98
39.76
69.31
44.88
Dynamic -T5-ase
70.69
1iff Dynamic Re-GEM
20
57.02
39.35
67.83
39,07
25
61.44
44,06
72,87
49.36
Table 6: Results based on different backbone model MAGPIE dataset.
1 Epoch
Epochs
Methods
BLEU
BLEU-
BLEU-
Rouge-1
Rouge-2
Rouge-L
BLEC
BLEU-2
BLE
Rouge-1
Rouge
Rouge-
Vanilla BART-lare
50.33
64.34
40.51
78.13
63.51
78.12
53.96
67.21
25.80
79.42
666.06
79:41
Dynamie
51.61
42.12
78.5%
64.42
57.94
69.38
46.77
81.54
68.49
81.54
Diff Dynumic+ Re-GEM
51.61
65.46
42.12
78.58
64.42
78.57
57.94
69.88
46.77
$1.54
68.49
$1.54
Vanilla IS-lnrge
50.53
64.54
40.51
78.13
63.51
78.12
53.96
67.21
24,80
79.42
66.06
79.41
Dynamic
Diff Dynamic Re-GEM
51,61
65,46
42.12
78.58
64.42
78.57
57.94
69.88
46.77
81,54
68.49
81.54
Vanilla TS-base
50.53
64.54
40.51
78.13
63.51
53.96
67.21
44.80
79.42
66.06
79.41
Diff Dynamic
$1.61
63.46
42.12
78.38
64.42
78.57
57.94
69.88
46.77
81.54
68.49
$1.54
Dynamic Re-GEM
51.61
46.77
81.54
Table Results based on different backbone model on MERMAID dataset.
D.4 Average Runtime
The whole training process for one epoch on two GPUs took approximately 40 minutes including 10 minutes for evaluating difficulties and 30 for fine-tuning.
E Case Study
In Table 9 and 10, we provide more generated examples from idiomatic expression generation and metaphor generation. Examples from differ- ent difficulty levels are selected for comparison. For both tasks, we could observe that most of the baseline models could correctly generate the get idiomatic expressions and metaphors when this example regarded as easy for the model. How- ever, when the example is selected from examples of medium difficulty levels, some baseline models start to generate wrong idiomatic expressions and metaphors. When it comes to the example from hard difficulty levels, all the baseline models can- not generate the correct idiomatic expressions and metaphors. Only our proposed method could still correctly generate the target idiomatic expressions and metaphors.
Therefore, these generated examples confirm that different examples have different difficulty lev- els for the models, which justify the need for cur- riculum learning. Besides, these examples also demonstrate that our proposed method could ef- fectively work by learning from an easy-to-hard order.

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Epuch
5 Epochs
Methods
Acc
BLEU
phrese-BLRU
Rouge
RRouge
phrase-BLL.
phrrase-Roge
Vanilla-BARRE Base-base
18
56.65
37.74
69.94
40.69
61.87
43.82
73.27
48.81
Dill Dynamic + Full Data -AART-
18 56.53
37.34
70.03
40.58
25
62.02
43.76
73.17
48.66
Dynamkc RE-EEM Full Data -ART--bse
56.33
70.11
40.79
44.01
Table 8: Results hased on full data on MAGPIE dataset.
Input Sentence
you would n't get anything from anywhere else because they 've cut it [MASK]. anywhere else because they "ve cut
Target Sentence
you n't get anything
hone
Vanilla
you wouldn't 't get anything from anywhere else because they don't 't cut it at the end the day.
Competence SL
wouldn't 't anything from anywhere else bocause they' 've cut by the back door
Easy
Competence WR
you would n't get anything from anywhen else bocause they 've cut to the hone.
Norm-hused
anywhere else hecause they "ve cut you woulda't It get anything from anywhere
you n't get anything
hone
SGCL fixed
the end day- wouldn't 't anything from anywhere else bocause they 'vc cut by the back door
because they don't It
SGCL + dynamic
Ours
you would n'l get anything from anywhere else bocause they "ve cut to the bone
Input Sentence
these were some of the qualities needed to [MASK] as sportsmen even at the more modest levels. these were come the qualities needed make grade as sportsmen even the more modest levels. these were some of the qualitics needed these were some of the gualities needed stand and be counted as sportsmen even at the more modest levels.
Target Sentence
Vauilla
occasion as sportsmen even the more modest lovels
Competence SL
Medium
Competence- WR
these were some of the qualities needed to make the killing as spottsmen even at the more modest levels. these were some of the qualities needed these were some of qualities needed thess were some of the qualities needed rise the occasion sportsmen even the more modest levels these were some of the qualities needed to make the grade as sportsmen even at the more modest levels. Are the Americans going [MASK] again, or is this an indictment which we should place on trial ?
Norm-based
to occasion as a sportsmen even the more modest levels
SGCL lixed
killing as sportsmen even
modest levels.
SGCL dynamic
Ours
Input Sentence
Target Sentence
Are the Americans going over the top again, or this indietment which should place on
Vauilla
the Americans going behind the scomes again. or this indictment which should place on this indictment which should place trial"
Competence SL
Are the Americans going behind the scenes again,
Hard
Competence- WR
Are the Americans going behind the scenes again, or is this an indictment which we should place on uial
Norm-hused
Are the Americans going behind the scenes again or is this an indictment which we should place on trial indictment which we should place trial Are the Americans going behind our backs again. is this indictment which we should place trial" ?
SGCL+ fixed
the Americans going through the motions again,
SGCL dynamic
Ours
Are the Americans going over the top again, or is this an indictment which we should place on trial"
Table A sample of the generated sentences on MAGPIE highlighting the correct idioms, and the wrong idioms. Easy represents the easy example randomly selected from the examples in the start after ranking based on difficulty levels. Medium represents the example randomly selected from the examples in the middle after ranking based on difficulty levels. Hard represents the example randomly selected from the examples in the final after ranking based on difficulty levels.

---

Input Sentence
Whose wrath loomed <V> heavy trojan towers:
Target Sentence
Whose wrath hung heavy o' trujan towers:
Vanilla
Whose wrath hangs heavy o l'or the trojan towers:
Competence +
Whose wrath heavy
trojan towers:
Easy
Competence +
Whose wrath hangs heavy o the trojan towers:
Norm-based
Whose wrath hangs heavy o' d'or the trojan towers:
Whose wrath hangs heavy o the trojan towers:
SGCL dynamic
wrath hangs heavy
trojan towers:
Whose wrath hung heavy
irujan lowers:
Input Sentence
To that which never can comfort <V> say <V>.
Target Sentence
To that which never can us comfort bring.
Vanilla Competence +
To that which never can comfort give
To that which never can comfort bring.
Medium
Competence +
To that which never can comfort find
Norm-based
To that which never can us comfort find
SGCL
To that which never can comfort make.
SGCL dynamic
that which never can comfort give that never can comfort bring.
Input Sentence
hardly knew when waves he <V> used <V>
Target Sentence
He handly knew when waves he tossed on
Vanilla
hardly knew when waves he plowed on
Competence +
hardly knew when waves he went He hardly knew when waves he pur on He hardly knew when waves he puf on He hardly knew when waves he rolled on hardly knew when waves he pur on hardly
Hard
Competence +
Norm-based
SGCL
SGCL dynamic
waves lossed on
Table 10: A sample of the generated sentences on MERMAID highlighting the correct metaphor, and the wrong metaphor. Easy represents the easy example randomly selected from the examples the start after ranking based difficulty levels. Medium represents the example randomly selected from the examples in the middle after ranking based difficulty levels. Hard represents the example randomly selected from the examples in the final after ranking based difficulty levels.


Reviews:
[{"review_id": "wn9c7I3khm", "reviewer": "Reviewer_gkQD", "summary": "", "questions": "The continual learning part seems to be used to alleviate the problem brought by CL applied to this task, but the works about CL before do not concern this. Does it mean the catastrophic forgetting problem just appear in non-compositional expression generation rather than other tasks?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1. Our primary objective is to emphasize the generation of non-compositional expressions rather than entire sentence construction. We want to generate non-compositional expressions to fit the given bidirectional context, which could not be accomplished by autoregressive generation. In this sense, we chose mask prediction as our format of the task.\n2. To date, there hasn’t been any prior research that has specifically highlighted the 'forgetting problem' in curriculum learning. We take pride in pioneering this observation and actively addressing the issue. Our hypothesis is that this problem is inherent in curriculum learning across various tasks. Given that curriculum learning inherently re-orders training samples from simpler to more complex sequences, there's a consequential shift in the distribution of these examples. This shift, we believe, potentially causes the forgetting phenomenon, irrespective of the specific task at hand. For our case of non-compositional expression generation, we propose a specifically designed continual learning to solve this problem. In this sense, the application of continual learning in curriculum learning for other tasks could be a broader research direction, which is out of the scope of this paper.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. First study on non-compositional expression generation including both metaphors and idioms. \n2. The novelty of the proposed method is adequate, which not only reflects on the new task-adaptive CL method but the new continual learning schema. \n3. This work also conducts extensive experiments, including comparison to other existing CL method, etc.\n", "reasons_to_reject": "In this paper, the task of non-compositional expression generation acts as mask prediction rather than autoregressive generation, which results in a certain degree of limitation. ", "questions_to_authors": "The continual learning part seems to be used to alleviate the problem brought by CL applied to this task, but the works about CL before do not concern this. Does it mean the catastrophic forgetting problem just appear in non-compositional expression generation rather than other tasks?", "paper_topic_and_main_contributions": "The paper aims to improve non-compositional expression generation that have not been explored much.\nThe main contribution is to propose a new curriculum learning approach adapted to non-compositional expression generation, and conduct continual learning to alleviate catastrophic forgetting in the curriculum learning. \n", "excitement": 4, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "1. Subscript error in line 317.\n2. The symbol is not clear in the Algorithm.\n"}, "extra_text": {}}, {"review_id": "6HTfi2LUoS", "reviewer": "Reviewer_vrK3", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1. Thank you for highlighting that point. More details can also be found in Appendix D. For clarity, we will consolidate the details. Additionally, we would like to note that we will be releasing our code upon acceptance as stated in the abstract, facilitating a more straightforward implementation.\n2. We apologize for any confusion caused. Due to page constraints, certain details were abbreviated. We will enhance the presentation for clarity, taking your feedback into consideration.\n3. Firstly, prior research, e.g. [1], has delved into the task of idiom generation using various models, including BART and T5 (specifically, T5-base). Based on these findings, BART-base outperformed the other models, which influenced our decision to adopt it as our primary model. Furthermore, the catastrophic forgetting issue can be fundamentally characterized as an overfitting problem. When models intensely adapt to new training samples, they inadvertently lose previously acquired knowledge from older examples. This phenomenon leads to the mentioned forgetting problem. Our adoption of continual learning primarily seeks to address this overfitting concern. Increasing the model's size could exacerbate overfitting by amplifying its complexity. Given the resource constraints of our tasks, leveraging more training data to offset this problem isn't feasible. To further substantiate our approach, we've included additional experiments showcasing the efficacy of our proposed method across diverse backbone models:\nWhen trained for one epoch:\n| Backbone Model | Methods                 | Acc | BLEU  | phrase-BLEU | Rouge | phrase_Rouge |\n|----------------|-------------------------|-----|-------|-------------|-------|--------------|\n| BART-large     | Vanilla                 | 20  | 58.43 | 40.24       | 71.88 | 42.77        |\n| BART-large     | Diff + Dynamic          | 22  | 60.47 | 42.83       | 74.02 | 45.71        |\n| BART-large     | Diff + Dynamic + Re-GEM | 25  | 61.73 | 44.24       | 75.12 | 46.83        |\n| | | | | | | |\n| BART-base      | Vanilla                 | 18  | 56.65 | 37.74       | 69.94 | 40.69        |\n| BART-base      | Diff + Dynamic          | 20  | 59.06 | 40.08       | 71.31 | 43.42        |\n| BART-base      | Diff + Dynamic + Re-GEM | 23  | 60.43 | 43.72       | 72.31 | 45.77        |\n| | | | | | | |\n| T5-large       | Vanilla                 | 18  | 56.21 | 37.13       | 68.74 | 39.23        |\n| T5-large       | Diff + Dynamic          | 20  | 57.94 | 39.85       | 70.03 | 41.73        |\n| T5-large       | Diff + Dynamic + Re-GEM | 22  | 60.74 | 42.23       | 71.94 | 43.72        |\n| | | | | | | |\n| T5-base        | Vanilla                 | 15  | 53.14 | 34.02       | 65.52 | 36.01        |\n| T5-base        | Diff + Dynamic          | 17  | 54.26 | 36.13       | 66.74 | 37.82        |\n| T5-base        | Diff + Dynamic + Re-GEM | 20  | 57.02 | 39.35       | 67.83 | 39.07        |\n\nWhen trained for 5 epochs:\n| Backbone Model | Methods                 | Acc | BLEU  | phrase-BLEU | Rouge | phrase_Rouge |\n|----------------|-------------------------|-----|-------|-------------|-------|--------------|\n| BART-large     | Vanilla                 | 27  | 62.73 | 45.02       | 74.98 | 50.33        |\n| BART-large     | Diff + Dynamic          | 28  | 64.55 | 47.83       | 77.02 | 53.11        |\n| BART-large     | Diff + Dynamic + Re-GEM | 29  | 66.02 | 49.13       | 78.94 | 55.03        |\n| | | | | | | |\n| BART-base      | Vanilla                 | 25  | 61.87 | 43.82       | 73.27 | 48.81        |\n| BART-base      | Diff + Dynamic          | 26  | 63.20 | 46.84       | 74.57 | 50.17        |\n| BART-base      | Diff + Dynamic + Re-GEM | 28  | 64.36 | 48.02       | 74.94 | 51.82        |\n| | | | | | | |\n| T5-large       | Vanilla                 | 24  | 60.12 | 42.98       | 72.74 | 48.02        |\n| T5-large       | Diff + Dynamic          | 25  | 61.03 | 45.02       | 74.38 | 50.12        |\n| T5-large       | Diff + Dynamic + Re-GEM | 27  | 62.91 | 47.03       | 76.55 | 51.82        |\n| | | | | | | |\n| T5-base        | Vanilla                 | 20  | 56.98 | 39.76       | 69.31 | 44.88        |\n| T5-base        | Diff + Dynamic          | 21  | 58.83 | 41.77       | 70.69 | 46.72        |\n| T5-base        | Diff + Dynamic + Re-GEM | 23  | 61.44 | 44.06       | 72.87 | 49.36        |\n\n\n[1] Zhou, Jianing, et al. \"Idiomatic expression paraphrasing without strong supervision.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. No. 10. 2022.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "- Non-compositional experssion generation is an interesting task\n- The experimental results are decent", "reasons_to_reject": "- The descriptions about implementation details are severely insufficient, making it difficult to reproduce this work. Specifically, the authors only describe a single sentence in Appendix B. \n- It is so confused about the backbone of the proposed model (as well as baselines) utill I read the last sentence in Appendix D.4, i.e., \"140M for BART(base)\". I think the authors should improve the presentation way.\n- The experiments are not enough, and the soundness of this paper does not reach the conference level. The authors only conduct experiments with the backbone of the BART base model. However, the size of backbone has a significant influence on the task performance. When the size rises, some issues (such as catastrophic forgetting) may not be challenging. Therefore, only using one backbone with one size is not enough.", "questions_to_authors": "", "paper_topic_and_main_contributions": "This paper focuses on the non-compositional expression generation task which aims to generate the masked idiom or metaphor in the input sentences. The authors propose a dynamic curriculum learning framework to sort the task samples from easy to hard. The difficulty of each sample is quantified under a special consideration of semantic gaps between the non-compositional expressions and the representations of their contituent words. To alleviate the catastrophic forgetting issue during curriculum learning, they also propose a continual learning strategy (named RE-GEM) inspired by GEM. Experimental results on two benchmark datasets (MAGPIE and MERMAID), and human evaluation show the effectiveness of the proposed method.", "excitement": 3, "reproducibility": 2, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "- Improve the implementation detail section"}, "extra_text": {}}, {"review_id": "VcNBk1kmU8", "reviewer": "Reviewer_ZgeL", "summary": "", "questions": "See weaknesses", "limitations": "", "rating": 3, "confidence": 4, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "1. While curriculum learning and continual learning are indeed established learning paradigms akin to supervised learning as a learning paradigm, our approach uniquely adapts these paradigms to our specific tasks. Specifically, (1) we introduce new difficulty metrics (Section 3.1) tailored to our challenges and pioneer a novel scheduling strategy for training examples (Section 3.2). Notably, we are the first to highlight the overlooked issue of forgetting in curriculum learning, a significant contribution to the field. Furthermore, rather than merely adopting existing continual learning methods, we have meticulously crafted our method (line383-line412) to cater to our task's nuances, making our method novel and inherently task-specific.\n2. We respond to this in two stages: (1) Using the full dataset post-curriculum learning would essentially revert to traditional, non-curriculum-based training. While this may mitigate the forgetting problem, it would also eliminate the improvements afforded by  curriculum learning. The crux of curriculum learning lies in reordering training examples from simpler to more complex tasks, contrasting with the random sequencing in standard training. Introducing the full dataset post-curriculum phase would disrupt this deliberate sequencing. (2) Our ablation study (Table 4) underscores the efficacy of our approach: models employing continual learning significantly outperform those without, demonstrated by automated metrics and via a more nuanced human evaluation. According to our ablation study (Table 4), with continual learning, we achieved improvements of 3 points on Accuracy (statistically significant), 3.64 on phrase-BLEU and 2.35 on phrase-ROUGE. According to our human evaluation (Table 5) which is more accurate, we achieved improvements of 1.12 on Meaning, 2.01 on Fitness and 1.11 on Overall.\n3. Previous research, as cited in [1], has investigated the idiom generation task using various models, including BART and T5 (T5-base). BART-base emerged as the superior performer, leading us to adopt it as our foundational model. It's also essential to recognize that the catastrophic forgetting issue is fundamentally rooted in overfitting. Models tend to lose previously learned information as they adapt to new training samples, precipitating this forgetting challenge. Continual learning serves as our strategy to counteract this. Upscaling the model would exacerbate overfitting, given the heightened complexity, especially in our low-resource setting where sourcing additional training data is impractical. Consequently, even larger architectures like T5-large are susceptible to this pitfall. We've included additional experiments showcasing the efficacy of our proposed method across diverse backbone models:\nWhen trained for one epoch:\n| Backbone Model | Methods                 | Acc | BLEU  | phrase-BLEU | Rouge | phrase_Rouge |\n|----------------|-------------------------|-----|-------|-------------|-------|--------------|\n| BART-large     | Vanilla                 | 20  | 58.43 | 40.24       | 71.88 | 42.77        |\n| BART-large     | Diff + Dynamic          | 22  | 60.47 | 42.83       | 74.02 | 45.71        |\n| BART-large     | Diff + Dynamic + Re-GEM | 25  | 61.73 | 44.24       | 75.12 | 46.83        |\n| | | | | | | |\n| BART-base      | Vanilla                 | 18  | 56.65 | 37.74       | 69.94 | 40.69        |\n| BART-base      | Diff + Dynamic          | 20  | 59.06 | 40.08       | 71.31 | 43.42        |\n| BART-base      | Diff + Dynamic + Re-GEM | 23  | 60.43 | 43.72       | 72.31 | 45.77        |\n| | | | | | | |\n| T5-large       | Vanilla                 | 18  | 56.21 | 37.13       | 68.74 | 39.23        |\n| T5-large       | Diff + Dynamic          | 20  | 57.94 | 39.85       | 70.03 | 41.73        |\n| T5-large       | Diff + Dynamic + Re-GEM | 22  | 60.74 | 42.23       | 71.94 | 43.72        |\n| | | | | | | |\n| T5-base        | Vanilla                 | 15  | 53.14 | 34.02       | 65.52 | 36.01        |\n| T5-base        | Diff + Dynamic          | 17  | 54.26 | 36.13       | 66.74 | 37.82        |\n| T5-base        | Diff + Dynamic + Re-GEM | 20  | 57.02 | 39.35       | 67.83 | 39.07        |\n\nWhen trained for 5 epochs:\n| Backbone Model | Methods                 | Acc | BLEU  | phrase-BLEU | Rouge | phrase_Rouge |\n|----------------|-------------------------|-----|-------|-------------|-------|--------------|\n| BART-large     | Vanilla                 | 27  | 62.73 | 45.02       | 74.98 | 50.33        |\n| BART-large     | Diff + Dynamic          | 28  | 64.55 | 47.83       | 77.02 | 53.11        |\n| BART-large     | Diff + Dynamic + Re-GEM | 29  | 66.02 | 49.13       | 78.94 | 55.03        |\n| | | | | | | |\n| BART-base      | Vanilla                 | 25  | 61.87 | 43.82       | 73.27 | 48.81        |\n| BART-base      | Diff + Dynamic          | 26  | 63.20 | 46.84       | 74.57 | 50.17        |\n| BART-base      | Diff + Dynamic + Re-GEM | 28  | 64.36 | 48.02       | 74.94 | 51.82        |\n| | | | | | | |\n| T5-large       | Vanilla                 | 24  | 60.12 | 42.98       | 72.74 | 48.02        |\n| T5-large       | Diff + Dynamic          | 25  | 61.03 | 45.02       | 74.38 | 50.12        |\n| T5-large       | Diff + Dynamic + Re-GEM | 27  | 62.91 | 47.03       | 76.55 | 51.82        |\n| | | | | | | |\n| T5-base        | Vanilla                 | 20  | 56.98 | 39.76       | 69.31 | 44.88        |\n| T5-base        | Diff + Dynamic          | 21  | 58.83 | 41.77       | 70.69 | 46.72        |\n| T5-base        | Diff + Dynamic + Re-GEM | 23  | 61.44 | 44.06       | 72.87 | 49.36        |\n\n4. To maintain brevity and coherence in the main text, we've allocated detailed experimental specifics to Appendix B and Appendix D, which we will consolidate for clarity of exposition. For transparency and the broader scientific community's benefit, we're also committed to releasing our code once our work receives approval, as mentioned in the abstract. \n\n[1] Zhou, Jianing, et al. \"Idiomatic expression paraphrasing without strong supervision.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. No. 10. 2022.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "Strengths:\n1) The first study on non-compositional expression generation, which may interest the research community.\n2) Presenting a framework for this task\n3) Better performance", "reasons_to_reject": "Weaknesses:\n1) I learn nothing after reading the method proposed in this work. The curriculum learning and continual learning are both existing methods. A task-specific method may be better.\n2) For catastrophic forgetting in curriculum learning, why not train the model with the full data after the curriculum learning stage and how is its performance? In my opinion, this issue may be not serious and experiments also show that it has only a little impact on performance.\n3) In line 8, you claimed a large pre-trained language model. However, the BART model you used in experiments is not a large version. So, is there the same trend under the larger pre-trained model or another different architecture (e.g., T5-large)?\n4) Lack of many experimental details, e.g., training details, lr, etc", "questions_to_authors": "See weaknesses", "paper_topic_and_main_contributions": "This paper focuses on non-compositional expression generation. The challenges of the task lie in the non-compositionality and limited data scale. To address the issues, the authors propose a curriculum-learning-based framework. During training, to mitigate catastrophic forgetting, the authors utilize continual learning. Experiments on both idiomatic expression generation and metaphor generation tasks show the effectiveness of the proposed approach.", "excitement": 3, "reproducibility": 3, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: This paper addresses an unexplored topic in non-compositional expression generation. The proposed method has been properly evaluated in comparison with various baselines, demonstrating the effectiveness of the proposed approach. A common complaint about the paper is the lack of enough details for the experimental setup. While releasing the code may help in addressing the reproducibility of the results, the paper itself should also contain enough details for understanding the proposed approach and the evaluation settings. I strongly encourage the authors to address the reviewers' concerns in the next revision.
Decision: Accept-Findings