GBT: Generative Boosting Training Approach for Paraphrase Identification
Authors: Rui Peng
Zhiling Jin
Yu Hong
Venue: EMNLP 2023 Findings (2023)
Keywords: Paraphrase Identification
Data Augmentation
Text Semantic Matching

Abstract: Paraphrase Identification (PI), a task of determining whether a pair of sentences express the same meaning, is widely applied in Information Retrieval and Question Answering. Data Augmentation (DA) is proven effective in tackling the PI task. However, the majority of DA methods still suffer from two limitations: inefficiency and poor quality. In this study, we propose the Generative Boosting Training (GBT) approach for PI. GBT designs a boosting learning method for a single model based on the human learning process, utilizing seq2seq model to perform DA on misclassified instances periodically. We conduct experiments on the benchmark corpora QQP and LCQMC, towards both English and Chinese PI tasks. Experimental results show that our method yields significant improvements on a variety of Pre-trained Language Model (PLM) based baselines with good efficiency and effectiveness. It is noteworthy that a single BERT model (with a linear classifier) can outperform the state-of-the-art PI models with the boosting of GBT.

GBT: Generative Boosting Training Approach for Paraphrase Identification
Rui Peng, Zhiling Jin, Yu Hong'
School of Computer Science and Technology, Soochow University, SuZhou, China (rpeng124, zhljinjackson, tianxianer)@gmaill
Abstract
Edit
Distance
type
Paraphrase Identification (PI), a task of deter- mining whether pair sentences express same meaning, is widely applied in Information Retrieval and Question Answering. Data Aug- mentation (DA) is proven effective tackling PI task. However, the majority of DA methods still suffer from two limitations: inefficiency and poor quality. In this study, propose the Generative Boosting Training (GBT) ap- proach for PL. GBT designs a boosting learning method for single model based the human learning process, utilizing seq2seq model to perform DA on misclassified instances period- ically. We conduct experiments the bench- mark corpora QQP and LCQMC, towards both English and Chinese tasks. Experimental results show that our method yields significant improvements on a variety of Pre-trained Lan- guage Model (PLM) based baselines with good efficiency and effectiveness. It is noteworthy that single BERT model (with linear clas- sifier) can outperform the nate-of-the-as PI models with the boosting of GBT.
(How fill the zip code of Taohho?)
WEE 23 TAR?
(How modify the zip cade Thohwo?)
WHl 25F O.
(How cun munagers employees to obey?)
25 M. 50
188
(How make employees obey the leuder?)
Table I: /BH-type cases in Chinese LCQMC corpus which fine-tuned BERT makes mistakes on. The sub- script of IBH is consistent with the original label.
Lyu et al., 2021; Xu al., 2022). The state-of- the-art methods achieve significant improvements, increasing PI performance up to the accuracy 91. 91.6% and 88.3% on the benchmark corpora QQP (Iyer et al., 2017) and LCQMC (Liu et al., 2018). No matter whether in the English or Chinese sce- nario, the advanced PI models still suffer from types of cases, as shown in Table The first ones are classified as "isomorphic but heterogencous" (denoted as I BB)),, which is referred to the pair of sentences holding similar syntactic structure but with different semantics (label "0"). Cases of this type tend to be literally consistent (smaller edit dis- tance), easily confusing models identify such pairs as "Paraphrase". The second ones are classi- fied as "isomerous but homogencous" (denoted IBH)). These cases are opposite of IB Hy. using different expressions to convey the same mean- ing (label "1"), which makes the model mispredict them as "oonpparaprras" due to the huge literal differences. We conduct pilot experiment on the QQP validation set. Among the incorrect predic- tions made by the fine-tuned BERT model, 35.9% of these cases correspond IBH, while 14.7% correspond to /HH, Errors in these two types occupy 50.7% 7% of all the errors made by the base- line model. highlights the ineffectiveness of the
1 Introduction
PI can be boiled down to a binary classification task, aiming to determine whether pair of sentences convey the same or similar meaning. It is funda- mental natural language understanding task with non-trivial challenges, serving practical tech- nique in the field of information retrieval and ques- tion answering (Hu al., 2014; Cer al., 2017; Rücklé al., 2020; Pang al., 2021).
A variety of neural PI approaches have been proposed (Chen et al., 2017; Wang et al., 2017; Zhang et al., 2017: Gong et al., 2017; Lai et al., 2019; Kim al., 2019). Recently, the large PL.- based Models are leveraged as crucial supportive encoders for neural PI (Devlin et al., 2019; Liu et al., 2019; Cui al., 2021; Zhang et al., 2021;
We use Levenshtein similarity with a threshold of 0.6 to identify literal similarity (sentences with a similarity greater than 0.6 are considered literally similar).
"Corresponding author.

---

current PI model in handling these cases.
ious PLM models. It is noteworthy that baseline model (with only a PLM encoder and linear clas- sifier) can outperform the advanced models with the boosting of GBT. Our contributions can be sum- marized as follow:
Advanced DA methods such Adversarial Training have been proven to be effective in solving such cases (Hou al., 2018: Kumar et al., 2020; Morris et al., 2020a; Li et al., 2022). However, advanced adversarial methods suffer from two lim- itations. The first one is inefficiency. The major- ity of these methods are based on greedy-basee synonym replacement (Li et al., 2018; Ren et al., 2019; Jin et al., 2020; Li et al., 2020; Garg and Ramakrishnan, 2020). They first need to ternal knowledge bases (e.g., WordNet) to obtain lists of synonyms. And after that, a greedy- based search strategy used to choose suitable words, which brings serious time costs. The second one is poor quality. Previous work (Morris et al., 2020a) has proven most of them may result in generating unnatural and unreadable sentences, or even huge semantic shifts from the original ones.
The proposed GBT brings stable and signif- icant improvements over the current PLMs- based PI models.
GBT utilizes the seq2seq model as the DA method, greatly improving the efficiency and quality of example generation.
GBT is vest-pocket, which extends the train- ing time by no more than 1 hour on an RTX3090 24GB GPU.
Related Work
PI has been widely studied due to its widespread application in downstream tasks. The existing ap- proaches can be divided into representation-basse and interaction- approaches.
With the continuous improvement of PLMs, gen- erative models, such as BART (Lewis al., 2020), are capable of generating sentences that are in line with human expression habits with acceptable time costs. Despite the fact that using seq2seq as DA method often requires additional data or special data conditions, PI data has a naturally generative feature with source and target sentences, making seq2seq a great DA method.
The representation-basee siamese architecture to encode two sentences into independent vectors of high-level features. On this basis, the semantic similarity is obtained by both feature vectors. He et al. (2015) propose a siamese CNN structure, and they verify the effectiveness of cosine similarity. Euclidean distance and element- wise difference. Wang et al. (2016) decompose two sentences the sets of similar and different kens extract similar and distinguishable features by CNN. Lai et al. (2019) focus on the ambigu- ity caused by Chinese word segmentation and pro- pose a suttice-based CNN model (LCNs). Lyu et al. (2021)'s LET-BERT perceives multi-gran multi-granularity in- teraction based on word lattice graph, where graph networks and external knowledge HowNei (Dong and Dong, 2003) are used, achieving state-of-the- art performance in Chinese PL.
approaches use a
We suggest that the existing PI models can be further improved in simple and effective way by strengthening learning on misclassified instances. Inspired by the human learning process. we believe that, just as humans learn knowledge from books, the model aims to learn previously unknown knowl- edge from training data. For the knowledge that is difficult to understand, we need to improve our understanding and cognition through repeated and extended learning, and so are the models.
Therefore, in this study, we propose the Gener- ative Boosting Training (GBT) method for single model, which utilizes seq2seq model as the DA method serving for our boosting learning algorithm. During the training process, GBT performs DA on the instances that the model did wrong in period of training steps, and enforces the model to be re- trained on these instances. this way, GBT can enable the model to correct its errors and enhance its ability to solve hard cases in a timely manner, just like human learning.
An interaction-basse PI model is designed per- ceive interaction features between sentences, and encode such information into the representations. Specifically, Wang et al. (2017) propose a BiMPM model under the matching-aggregaen which conducts one-to-mant token-level matching operations to obtain interaction information. ESIM model proposed by Chen et al. (2017) performs interactive inference based on LSTM representa- tions. ESIM sharpens the interaction information by means of element-wise dot product and subtrac- tion. Inspired by ResNet (He et al., 2016a), Kim
framework,
Experiments on the benchmarks of English QQP and Chinese LCQMC show that GBT improves var-

---

al. (2019) propose a densely-connected recur- rent and co-attentive Network (DRCN), which com- bines residual operation with RNN and attention mechanism. Zhang et al. (2021) propose a Relation Learning Network -Re- based on BERT, which is characterized by interactive relationship percep- tion of multi-granularity linguistic units. The re- cently proposed ISG-BERT (Xu et al., 2022) inte- grates syntactic alignments and semantic matching signals of two sentences into an association graph to gain a fine granularity matching process.
Augmented S2 MRA Paraphrase (How
earhh should (10 Taoboo
Original
(How Troboo code7)
X: W
(How to mosily codo7)
Xg. (2:3 2
copy
Nugmented NutsPart
X 1
F M (H0 check Triboo code
(How to Ail Tacteo ap oode??
Levinaltain Dartente
Figure 1: Utilizing Gp and GN to perform DA to gen- erate two augmented data (only on X)).
DA techniques such as Adversarial Training have received keen attention in solving seman- tic matching tasks. Ren et al. (2019) propose Probability Weighted Word Saliency (PWWS), determining word substitution order by the word saliency and weighted by the classification prob- ability, where WordNet (Miller. 1995) used for synonym replacement. Similar to PWWS, Li et al. (2018) propose TextBugger, which finds word sub- stitution order by Jacobian matrix and considers both character and token-level modification. The BERT-Attach proposed by Li et al. (2020) also applies a greedy word saliency strategy find vul- nerable words. It uses BERT-MLM for word gen- eration instead of synonyms.
learned knowledge to infer other instances. In addi- tion, models need timely correct mistakes during the learning process.
In light of this, we devise a simple but effective training method GBT to mimic the human learning process. First, we leverage the inherent proper- ties of PI data to transfer it into seq2seq training data, enabling the training of two independent gen- erators. During training, both generators need to perform DA on the cases where the model made mistakes. Subsequently, we re-train the model on the augmented data at a certain interval.
3.1 Baseline Model
With the advancement of auto-regressive PLM, the seq2seq model is also used as a DA method in various scenarios (Hou et al., 2018; Kumar et al.. 2020; Li al., 2022). However, seq2seq is not generalized as a DA method such as synonym re- placement, because requires the data to have a generative condition, which relatively strict. In this paper, we explore a generative augmentation method suitable for PI.
As introduced by Devlin et al. (2019), we adopt the model framework of a PLM-based encoder with a two-layer classifier as our baseline model. The model is designed to predict the binary relationship of X1 and X2. The input sequence is organized as X [[CLS), X1, [SEP), (SEP)) for BERT- based models and X ( X1, <\,>>, X2, <\8>> for RoBERTa-based models, where [CI.S), [SEP], 
 and <),>> serve as special tokens. Further, we input X into the PLM encoder to produce its con- textual representations of each token. The [CLS] or 
 token is fed into linear classifier to estimate the probabilities p(ù X X2). The optimization objective is to minimize the cross-entropy loss:
3 Approach
Let us first define the PI task in formal way. Given pair of sentences X1 = (xt, =q...... 1] and X2 { x2_)..") Pl model aims to determine whether X1 and X₂ paraphrases of each other. During training, each data example can be noted as (X1, X2, y), where y stands for a binary label indicating the relationship between X1 and X2: either "Paraphrase" (P) or "Non-paraphrase" (N). The goal of training is to optimize the PI model f(X,, X2) by minimizing the prediction loss. We suggest that the learning process of the model closely parallels that of humans. The goal is to learn previously unknown knowledge from one instance, reinforce impressions, and use the
Lp (y (og(p(0)X1, +(1 y) log(1 r(ū) X1, X2))).
(1)
3.2 Boosting Generator
For DA, we train two seq2seq models Gp and G as our generators. Given an input sentence, the Gp aims produce a paraphrased sentence that is literally different (HH.). While G is to generate a non non- pon-paapphrased sentence that is literally similar to the input sentence (IB Ho).

---

BART (Lewis et al., 2020): BART is classi- cal seq2seq model using the standard Transformer structure including a bidirectional encoder and an autoregressive decoder. It is pre-trained by cor- rupting text with an arbitrary noising function, and learning to reconstruct the original text. We use BART as the backbone model for training both generators Gp and GN.
The generator is evaluated by the BLEU metric. The final BLEU value for fine-tuned Chinese gener- ator 51.5 (Gp) and 48.8 v), while that for the English generator is 29.6 (G)) and 27.7 (Gx). Af- ter training, the parameters of Gp and G are fixed for subsequent use, noted as Boosting Generator.
3.3 Boosting Training
Data Preparation: We introduce the training data for two generators Gp and Gy. First, we split the original Pl training set into Paraphrase (P) and Non-Paraphrase (N) by the given binary label. We take 1, as the source sentence and X2 as the target sentence no matter on P or N.
Classical boosting learning methods perform en- semble learning. During training time, the inte- grated model serially adds new models to itself to strengthen the cases where the previous integrated model did wrong. In this way, the integrated model is able to enhance the ability to handle hard cases. Different from the classical boosting method, GBT repeatedly enhances the ability of a single model to solve hard cases without the massive cost of multiple models. In a round of learning, GBT records wrong instances during fixed training steps (denoted boosting interval t). The boost- ing interval & controls how often the model will reinforce learning on the wrong instances. In order to be corrected time, the interval t should not be too large. Because the model will have a large deviation from the previous model that did wrong at that time due to parameter updating. the effect of re-learning wrong instances will also decrease. After every learning steps, the model will enter boosting training mode from the normal training.
For data P. we aim to select paraphrased sen- tence pairs that X1 and X2 are literally different to train Gp- In this way, Gp is able to learn how generate paraphrased sentences with different syntactic structures from the source sentence. To be specific, we calculate the BLEU² score for each pair of X1 and X2 in data P. Then, we randomly select 50k examples from the top 50% of pairs with the lowest BLEU scores (a lower BLEU score indicates a greater dissimilarity). These selected examples are used as the training data for Gp.
While for data N, we select non-paraphrased sentence pairs that X1 and X2 are literally similar. which enables n to produce target sentences with similar literal content but different semantics. For this, we also calculate the BLEU score of data N and randomly select 50k examples from the top 50% of pairs with the highest BLEU score as training data for Gn-
In boosting training mode, wrong cases will be augmented by Gp and GN as shown in Figure 1. Note that different from Figure 1, we perform DA not only on X1 but also X2. Given a wrong in- stance (X,, X2, y), we will generate four extra cases according to it. First, Gp produces para- phrase sentences of X1 and X2 respectively, and we note them as XPP and X.P. G is also used to generate non-paraphrase sentences of X, and X2, denoting as X and X, In this way, we are able to produce two T H1 cases (X,, x/". 1) and (X2, X,. 1), also two IBHo cases (X1, X;V, 0) and (X2, X 0) according to one wrong instance.
Training Process: We train the generators Gp and GN by fine-tuning the backbone model BART. Specifically, given training data (X,, X2), the source sentence X1 fed into the encoder, and the decoder is required to generate tokens in target sentence X2 auto-regressively. The Gg is trained to maximize the ligeliiklihood:
T LCen (0) = Σ log(po(z2)a (2) (2)
The GBT learning process is shown in Algo- rithm 1. Note that we randomly select p% cases for boosting training (L9 in Algorithm 1). This is generate are relatively difficult, and the proportion of difficult examples should not be too high. Besides, did not start the first boosting training until the warm-up steps ends (L8 in Algorithm 1). We will discuss the boosting interval t, boosting ratio p. and the starting timing
where x? represents current generating word in X2. and x2,-1 represents previous word in the ground-truth rather than word af. generated by the model. This training technique is known as teacher forcing.
because the examples
"Our choice of BLEU, instead of other evaluation metrics, is motivated by our specific aim to measure literal similarity. In this context, BI BL adequately satisfics our purpose and provides computational efficiency.

---

Algorithm 1: Boosting Training
Dataset
Size
Pos:Neg
Domain
Input: original example X = X2],
LCQMC
260.068
1:0.7
label y, boosting generator Gp(.),
QQP
404,276
1:2
Open-Domain
Gx(-) boosting interval t, boosting ratio target model [()), warm-up steps training function train(.)
Table Statistics for LCQMC and QQP
Output: None
4.2 Hyperparameter Settings
Define train steps ts, W of wrong cases set;
Our experiments conducted HuggingFace's Transformers Library. We adopt an Adam opti- mizer with epsilon of 1e-8 and warm-up steps ratio of 10% for all tasks. For the generative model, we first fine-tune the BARTlarge models (the Chinese version is provided by fulp") on both benchmarks (Section 3.2). The batch size and learning rate are set to 16 and 2e-5. We set the epoch to 3 and the max sequence length is set to 100.
2 Begin training;
3 For X in TrainData Do:
4 +=+=1
5
train(f, X)
If f(X)!= y Do:
W.ddd(X)
If ts = 0 and is > us Do:
W-> randomly save p% cases Boosting Train(f, W)
10
the learning rate 2e-5 and batch size to 32 for LCQMC. While for the English task, the learning rate and batch size are set to 2.5c-5 and 64. The fine-tuning epoch is set to 5 for LCQMC. While for QQP, we fine-tune the models for 50k steps, and checkpoints are eval- uated every 2k steps. We set the boosting interval t = 500 steps and boosting ratio p 25(%). All experiments conducted single RTX 3090.
For Chinese PI tasks,
11
W.clear)
12 Boosting_Traiinn
W):
13
For X in W Do:
14
Gp(X1) + x
Gp(X2)+AX -
GN(Xi) X (x(2))+XX
W.add((X1,2)) 1), (X2,
W. Waddd((X) X 0), (X2, x2,x3,
train(/, W)
4.3 Main Results
in Section 4.5.
Table 3 and Table show the main results on LCQMC and QQP. The reported performance of our models are average scores obtained five runs with random seeds.
The model required to be re-trained on the wrong instances and their augmented ones to en- hance performances. After that, the model will return to normal training steps to continue new round of learning.
LCQMC: The Chinese PI models are listed in Table 3. Our proposed GBT yields significant im- provements (p-value < 0.05 in statistical signifi- cance test) over baselines, which demonstrates that GBT generalizes well when cooperating with dif- ferent PLMs. The most substantial boosting up to 3.5% (Acc.) and 2.5% (Fi), achieving state-of- the-an art performance.
4 Experimentation
4.1 Corpora and Evaluation Metrics
We evaluate our PI models on two benchmark cor- pora, including Chinese LCQMC (Liu al., 2018 0018), well as English QQP (Iyer al., 2017). Both QQP and LCQMC are large-scale corpora for open- domain, where sentence pairs are collected from QA websites without rigorous selection. Each in- stance both corpora is specified as pair of sen- tences. The binary labels of "Pos" (i.e., paraphrase) and "Neg" (i.e., non parappararrss are provided. Ta- ble 2 shows the statistical information of the cor- pora. The canonical splitting method of training. validation and test sets for the corpora is plainly stated, and we strictly adhere to We evaluate all the models the experiments using accuracy (ACC.) and Fl-score.
In addition, our models outperform all state-of- the-art models, including the recently-proposed LET-BERT which addresses issues of complex Chi- word segmentation and ambiguity to some extent. It uses HowNet the exteral knowledge base provide explanations. While for GBT, it is noteworthy that we use no extra knowledge. GBT takes advantage of the existing data to train the Boosting Generator, and only costs some additional time for DA and boosting training during the train- ing process. In this way, baseline model without
Phttps://nke wudan.edu..co edu.cn

---

LCQMC
QQP
Models
Models
ACC.
F1
ACC.
F1
Text-CNN (He et al., 2016b)
72.8
75.7
CENN (Zhang el al., 2017)
80.7
BiLSTM (Mueller and Thyagurajan, 2016)
76.1
78.9
L.D.C (Wang et al., 2016)
85.6
Laltice-CNN (Lai et al., 2019)
82.4
BiMPM (Wang al., 2017)
88.2
BiMPM (Wang et ul., 2017)
83.3
84.9
DIIN (Gong al., 2017)
89.1
ESIM (Chen al., 2017)
82.5
84.4
DRCN (Kim et al., 2019)
90.2
GMN-BERT (Chen al., 2020)
87.3
88.0
R*-Net (Zhang al., 2021)
91.6
LET-BERT (I.yu et al., 2021)
88.3
88.8
ISG (RoBERTa-large) (Xu et al., 2022)
91.4
BERT (Devlin et al., 2019)
85.7
86.8
BERT (Devlin et al., 2019)
89,4
87.5
+ GBT
89,2
89.3
+ GBT
91.5
88.7
BERT-wwm (Cui 2021)9
86.8
87.7
BERTiagg (Devlin et al., 2019)
89.6
87.8
+ GBT &
89.4
89.5
GBT
91.7
88.9
MacBERT (Cui et al., 2021)0
87,0
88.0
GBT
89.6
89.7
RoBERTa (Liu et al., 2019)9
89.9
88.4
GBT
91.8
89.0
MacBERToorge (Cui al., 2021)
87.6
88.3
+ GBT
89.9
89.9
RoBERTaaarge (Liu et al., 2019)
90.0
89.1
GBT
92.3
89.6
Table 3: Comparison the performance (%) of base- lines and previous work on Chinese LCQMC, The mark "Q" denotes the PL.M-based baselines, while "&" de- notes GBT boosted models that obtain significant im- provements (p-value < 0.05 in statistical significance test) over baselines.
Table 4: Comparison to the performance (%) of base- lines and previous work English QQP. The mark ">" denotes the PLM-based baselines, while " denotes GBT boosted models that obtain significant improve- ments (p-value 0.05 in statistical significance test) over baselines.
any enhancing component network outperform the existing strongest model.
shorter and the difficulty lower, which makes the generation effect better. Therefore, the boosting effect for Chinese PI is better than that of English.
Quora: Table 4 shows the performance of En- glish PI on QQP. The previous work didn't 't report Fl-scores on QQP. We evaluate our models with the Fl metric and report the performance to support future comparative studies. Similarly, GBT boosts baseline models stably on different configurations and PLMs, achieving statc-of-the-ar performance.
4.4 Ablation Study
We conduct ablation experiments over the base- line model using BERT as described in Section 3.1. Three expanded models are considered in this ex- periment. First, "+Only Boosting Generator" uses Boosting Generator to generate augmented cases all training data matter whether the model did right or wrong, which makes the training data four times larger than before. Second, "+ Only Boosting Train" applics the Boosting Training al- gorithm in Algorithm but only re-train on the original wrong cases with no Boosting Generator for DA. Last, the GBT combines both aforemen- tioned approaches to enforce the GBT approach.
The previous approach Re-Net is strongest among the advanced models. Its structure con- tains both CNN and PLM-based encoder. Besides, it is learned by utilizing the label information of instances, and additionally trained by congenial- ity recognition among multiple instances. Our ap- proach achieves comparable performance R²_ Net when BERTbase used as the backbone, but with no sophisticated structure and learning pro- cess. The recently proposed ISG model is an- other powerful model, using a graph model to in- tegrate syntactic alignments and semantic match- ing signals. Our model outperforms ISG when RoBERTalarge is used with extra parameters and structure. In the English scenario, GBT also brings strong improvements over the baseline mod- els to achieve state-of-the-arr performance.
Table 5 shows the performance obtained on test sets. It is found that the performance has sharply dropped with Boosting Generator performing on the full amount of data. This is due to the unbal- anced proportion of difficult instances and redun- dant samples. While only applying Boosting Train- ing simply on the original wrong cases brings sub- stantial improvements. This demonstrates that de- spite not utilizing Boosting Generator for DA, our boosting training algorithm an effective means of improving performance.
We can observe that improvements in Chinese PI are more than English. This is partly due to the effect generator models under different language scenarios. The generation length in Chinese is
Last, we prove that GBT combines Boosting

---

LCQMC
QQP
LCMOC
LCQMC
Models
ACC.
F1
ACC.
F1
BERT
85.7
86.8
89,4
87.5
86
Only Boosting Generator
79.4
81.0
81.2
83.1
Only Buosting Training
88.2
88.6
90.9
88.1
100
booating interual t (xteps)
250
500
1000 ltp None
0
hooeting rhio
10
25
50
75
100
+GBT
89.2
89.3
91.5
88.7
QQP
QQP
Table 5: Ablation study on both benchmarks.
ALE:::119
92
91
90
89
87
Training with Boosting Generator to achieve fur- ther improvements. Boosting Training algorithm finds suitable instances and timing for Boosting Generator to make DA, while the latter brings stronger improvements.
100 250
1000 1xp kane boosttng interval a (stmps)
500
boosting P
Figure 2: Effectiveness of boosting interval and boost- ing ratio p on both benchmarks.
4.6 Comparison with advanced DA methods
4.5 Boosting Timing and Ratio
Compared with advanced DA methods, GBT has the advantage of generating high-quality instances with low time cost because we use the seq2seq model (Boosting Generator). We replace the Boost- ing Generator with advanced DA methods to an- alyze their efficiency and performances as shown in Table 6. All adversarial methods used baselines are open-source and readily applicable to the PI task. We implement these methods using TextAttack* (Morris et al., 2020b). We conduct experiments on the QQP datasct, because under the Chinese scenario, there are few DA-related re- searches that we can compare with. We compare our models to series of strong arts, including:
As mentioned in Algorithm 1, we use boosting in- terval to control the boosting timing, and boosting ratio 5% to control how many instances should be augmented for difficulty balance. Figure shows how the interval and ratio p affect model learning,
We conduct experiments when the boosting in- terval t is in { 100, 250, 500, 1000, lep), where lep means we perform the boosting learning at the end of each epoch. As expected, the smaller the interval brings better performance, because the model can correct mistakes and learn expanded knowledge (augmented examples) in a timely manner. How- ever, frequent switching of training modes will lead to certain amount of extra cost. Therefore, we believe t = 250 is good choice.
PWWS (Ren et al., 2019): Based the synonym replacement strategy, PWWS pro- word salience approach to determine word replacement or- der. The synonyms are provided by WordNet.
Moreover, we also test the boosting ratio p on both benchmarks where takes values from ( (10, 25, 50, 75, 100) (%). We set boosting ratio p 25% as reasonable value. It can be observed that if p is too small, the enhanced effect will not be obvious. While if p too large, it will lead to an imbalance in the proportion of difficult examples, eventually affecting the performances.
poses probability
TextBugger (Li et al., 2018): TexiBugger finds important words by Jacobian matrix and greedy search. In order to ensure that the gen- erated examples are literally and semantically consistent with the original ones, character and word-level modifications are considered.
Just like the human learning process, bet- ter to learn difficult knowledge after mastering a certain foundation instead of learning difficulties at the beginning. Therefore, we test three timings on when we should start first boosting training, re- spectively at 1) the beginning of the whole training, after the first epoch (start at the second epoch) 3) after the end of the warm-up steps. As expected, start timing at 1) performs worst and 3) works best. That is why we do not start boosting training until the warm-up steps end (L8 in Algorithm 1).
BERT-Attack (BERTA) (Li et al., 2020): BERTA identifies vulnerable words by loss- based scores, which to mask tokens one by one to see the loss changes. The BERT MLM is used to generate top- candidates for each keyword, and another greedy algorithm is ap- plied to confirm the final option. Note that
'https: tttttt//acc//mmtaaeeeettttttkkkk

---

QQP
Boosting instances
Time cost (sec. per instance)
Models
Generation method
ACC.
F1
(total)
BERT (Devlin et al., 2019)
89.4
87.5
0
None
GBT (PWWS) (Ren et al., 2019)
89.9
87.9
20984
0.42 (699.7%)
Greedy Synonym
GBT (TexiBugger) (Li al., 2018)
90.5
88.4
20700
0.21 (350.0%)
Greedy Gradient
GBT (BERTA) (I.i ct al., 2020)
90.8
88.4
21132
0.61 (1016.7%)
Greedy + MI.M
Curos (Boosting Generator)
91.5
88.7
15144
0.06 (100.0%)
Seq2seq (BARTlarge)
Table 6: Time efficiency and generated effectiveness comparison between our Boosting Generator and advanced DA methods on English QQP.
default K is set as 48, but the time cost is way too much and we regulate K as 8.
Sentences
Label
X, How do remove wart?
Original
X2 How do remove wart on my face?
X:: How du slay a want?
PWWS
As mentioned, most previous DA methods are based on greedy search for word replacements, and there also few methods using MLM for word generation. However, most methods have been criticized for their efficiency or generation qual- ity shortcomings (Morris et al., 2020a). With the progress of the generative model, we believe that the generative model is far better than rule- based methods under certain conditions, and it can per- form well in sentence diversity and fluency.
Xg: How remove wart my face?
X1: How remove wart?
TextBugger
X2: How do eliminate wart on my face?
X: How do I replace want?
BERTA
Xy: How do crase wart on my face?
Gp(X)
X;: How remove warf?
(IBIT))
X²:: What the best way remove wart?
GyLXD
X, How do remove wart?
(TRH))
X." Why do you remove a wart?
Gy-Xx,)
Xy: How do remove wart on my face?
(BHH))
X.f:: What the way remove on face!!
Gw(Xg)
Xg: How do remove wart my face?
(BBH))
X²" Why remove wart your face??
Table 7: Examples of data augmentation with GBT and other DA methods.
As shown in Table 6, GBT has much higher generation efficiency in Time cost. Our GBT with scq2seq generator is several times more efficient than advanced methods, proving that the generative model able to achieve the expected effect. The generation quality can be evaluated through the comparison in performance. The model training on the instances generated by our Boosting Genera- tor brings the strongest improvements. Meanwhile, GBT with our Boosting Generator produces fewer boosting instances, proving that our model has rel- atively fewer wrong instances during training.
tively (as described in Section 3.3). Our generated sentence is marked in blue. While the modifica- tions of other DA methods are marked in red.
PWWS and TextBugger replace the word "re- move" " with "slay" and "eliminate" respectively. Although the generated sentence is semantically similar to the original sentence, it is unnatural and does not conform to normal pragmatic habits. BERTA replaces "remove" in both sentences with "replace" and "erase", resulting in semantic shifts from the original pair. Moreover, all three methods generate "Paraphrase" sentences that are literally similar to the original ones, which makes the model easy to recognize.
In addition, it can also be found that despite re- placing different DA methods, the model still gains substantial improvements, demonstrating the gen- eralization performance of our Boosting Training algorithm. Moreover, when the DA method is re- placed, our GBT is not limited to the PI task but can be generalized to any task.
It can be observed that our generative approach can not only generate -Non-Paraphrase" examples but also produce more natural and diverse results. When generating "Paraphrase", it recognizes the se- mantics between "How" and "What is the best way" are similar, changing the syntactic structure while preserving the same meaning (IBH)). While for -onapaaappraase. it switches How-type questions to Why-rype questions, as to preserve similar literal structure while changing the semanties Ho). In this way, our GBT is able to bring better effective- ness during the training process.
4.7
Case study
GBT provides a time-efficient DA method as de- scribed in Table 6. Compared with other DA meth- ods, the performance improvement of GBT proves the excellent generation quality to certain extent. We show an English case in QQP generated with different DA methods in Table
As shown, four examples are produced by using Gp and G to generate from X, and X₂ respec-

---

5 Conclusion
matching networks for Chinese short text matching. In Proceedings of the 58th Annual Meeting of the As- sociation for Computational Linguistics, pages 6152- 6158, Online. Association for Computational Lin- guistics.
In this study, we propose a Generative Boosting Training (GBT) approach for Pl. GBT combines the generative DA method with our boosting learn- ing algorithm, which enables the model to cor- rect mistakes and perform expanded learning in a timely manner during the learning process. GBT offers several advantages: high-quality and effi- cient DA for Pl, simple and effective boosting learning algorithm, and state-of-the-an matching performance. Experimental results on both English and Chinese benchmarks verified the effectiveness and efficiency of GBT. In the future, we will ex- plore more generalized generative DA methods to broaden the application of GBT various tasks.
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen. 2017. Enhanced LSTM for natural language inference. In Proceedings of the 55th ACL, pages 1657-1668.
Yiming Cui, Wanxiang Che, Ting Liu, Bing Qin, and Ziqing Yang. 2021. Pre-training with whole word masking for chinese bert. IEEE/ACM Transac- tions on Audio, Speech, and Language Processing. 29:3504-3514.
Jacob Devlin, Ming-Wor Wei Chang. Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of NAACL 2019, pages 4171-4186.
Limitations
GBT leverages the seq2seq model as a high-quality and efficient DA method. However, the seq2seq method requires data with the generative property (such data for PI), which limits the generaliza- tion of our DA method to other tasks. In contrast, method that can be generalized to other tasks. Given the remark- able ability of Large Language Models (LLMs) to adhere to instructions, we can use LLMs to gener- ate data tailored to the specific task by providing well-designed prompts. In this way, our GBT can achieve generalization to other tasks.
Zhendong Dong and Qiang Dong. 2003. Hownet-a hybrid language and knowledge resource. In NLP. KE 2003. Proceedings. 2003, pages 820-824. IEEE.
Siddhant Garg and Goutham Ramakrishnan. 2020. Bae: Bert-based adversarial examples for text classifica- tion. arXiv preprint arXiv:2004.01970.
our boosting learning algorithm
Yichen Gong, Heng Luo, and Jian Zhang. 2017. Natural language inference over interaction space. arXiv preprint 1oKt::/709.04445
Hua He, Kevin Gimpel, and Jimmy Lin. 2015. Multi- perspective sentence similarity modeling with convo- lutional neural networks. In Proceedings of EMNLP 2015, pages 1586-1586.
Furthermore, GBT may not handle noisy exam- ples with incorrect labels well, as it tends to re- peatedly emphasize the influence of such examples during the training stage. This issue of mislabeling can be mitigated through data-cleaning approaches.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016a. Deep residual learning for image recog- nition. In Proceedings of CVPR 2016, pages 770- 778.
A Acknowledgements
Tong He, Weilin Huang. Qiao, and Jian Yao. 2016b. Text-attentional convolutional neural network for scene text detection. IEEE TIP, 25(6): 2541.
We thank all anonymous reviewers for their insight- ful comments. This work is supported by National Key R&D Program of China (2020YFB15) 131166011) and National Science Foundation of China (62376182, 62076174).
Yutai Hou, Yijia Liu, Wanxiang Che, and Ting Liu. 2018. Sequence-to-sqquence data augmentation for dialogue language understanding. In Proceedings of the 27th International Conference Computa- tional Linguistics, pages 1234-1245, Santa Fe, New Mexico, USA. Association for Computational Lin- guistics.
References
Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez- Gazpio, and Lucia Specia. 2017. SemFvval-2017 task Semantic textual similarity multilingual and crosslingual focused evaluation. In Proceedings of the 11th SemEval, pages 1-14.
Baotian Hu, Zhengdong Lu, Hang Li. and Qingcai Chen. 2014. Convolutional neural network architectures for matching natural language sentences. NIPS, 27.
Lu Chen, Yanbin Zhao, Boer Lyu, Lesheng Jin, Zhi Chen, Su Zhu, and Kai Yu. 2020. Neural graph
Shankar Iyer, Nikhil Dandekar, and Kornel Csernai. 2017. First quora dataset release: Question pairs.

---

Di Jin, Zhijing Jin, Joey Tianyi Zhou, and Peter Szolovits. 2020. Is bert really robust? a strong base- line for natural language attack on text classification and entailment. In Proceedings of the AAAI con- ference on artificial intelligence, volume 34, pages 8018-8025
George A Miller. 1995. Wordnet: lexical database for english. Communications of the ACM, 38(11):39
John Morris, Eli Lifland, Jack Lanchantin, Yangfeng Ji. and Yanjun Qi. 2020a. Reevaluating adversarial ex- amples in natural language. In Findings of the Asso- ciation for Computational Linguistics: EMNLP 2020, pages 3829-3839, Online. Association for Computa- tional Linguistics.
Seonhoon Kim, Inho Kang, and Nojun Kwak. 2019. Semantic sentence matching with densely-connected recurrent and co-attentive information. In Proceed- ings of AAAI, volume 33, pages 6586-6593.
John Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, and Yanjun Qi. 2020b. TextAitack: frame- work for adversarial attacks, data augmentation, and adversarial training in NLP. In Proceedings of the 2020 Conference on Empirical Methods in Natu- ral Language Processing: System Demonstrations, pages 119-126, Online. Association for Computa- tional Linguistics.
Varun Kumar, Ashutosh Choudhary, and Eunah Cho. 2020. Data augmentation using pre-trained trans- former models. In Proceedings of the 2nd Workshop on Life-long Learning for Spoken Language Systems, pages 18-26, Suzhou, China. Association for Com- putational Linguistics.
Yuxuan Lai, Yansong Feng, Xiaohan Yu, Zheng Wang. Kun Xu, and Dongyan Zhao. 2019. Lattice cnns for matching based chinese question answering. In Proceedings of AAAI, volume 33, pages 6634-6641.
Jonas Mueller and Aditya Thyagarajan. 2016. Siamese recurrent architectures for learning sentence similar- ity. In Proceedings of AAAI, volume 30.
Liang Pang, Yanyan Lan, and Xueqi Cheng. 2021. Match-ignition: Plugging pagerank into transformer for long-form text matching. In Proceedings of the 30th CIKM, pages 1396-1405.
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. peeeence-t-sequeeee pre-training for natural language generation, translation, and com- prehension. In Proceedings of the 58th Annual Meet- ing of the Association for Computational Linguistics. pages 7871-7880, Online. Association for Computa- tional Linguisties.
BART: Denoising
Shuhuai Ren, Yihe Deng, Kun He, and Wanxiang Che. 2019. Generating natural language adversarial exam- ples through probability weighted word saliency. In Proceedings of the 57th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 1085- 1097, Florence, Italy. Association for Computational Linguistics.
Bohan Li, Yutai Hou, and Wanxiang Che. 2022. Data augmentation approaches in natural language pro- cessing: A survey. AT Open.
Andreas Rticklé, Jonas Pfeiffer, and Iryna Gurevych. 2020. Multicqa: Zero-shot transfer of supervidd text matching models on a massive scale. arXiv preprint arXiv:2010.00980.
Jinfeng Li. Shouling Ji, Tianyu Du, Bo Li, and Ting Wang. 2018. Textbugger: Generating adversarial text against real- world applications. arXiv preprint arXiv: 1812.0881..
Zhiguo Wang, Wael Hamza, and Radu Florian. 2017. Bilateral multi-perspective matching for natural lan- guage sentences. arXiv preprint arXiv: ftdv:1700 1702.03814.
Linyang Li, Ruotian Ma. Qipeng Guo, Xiangyang Xue, and Xipeng Qiu. 2020. BERT-ATTACK: Adversar- ial attack against BERT using BERT. In Proceed- ings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 61 6193-6202, Online. Association for Computational Linguistics.
Zhiguo Wang, Haitao Mi, and Abraham Ittycheriah. 2016. Sentence similarity learning by lexical decomposition and composition. arXiv preprint arXiv.1602.07019.
Chen Xu, Jun Xu, Zhenhua Dong, and Ji-Rong Wen. 2022. Semantic sentence matching via interacting syntax graphs In Proceedings of the 29th Inter- national Conference Computational Linguistics. pages 938 949.
Xin Liu, Qingcai Chen, Chong Deng, Huajun Zeng, Jing Chen, Dongfang Li, and Buzhou Tang. 2018 Leqme: A large-scale chinese question matching corpus. In Proceedings of the 27th ACI., pages 1952-1962.
Kun Zhang, Enhong Chen, Qi Liu, Chuanren Liu, and Guangyi Lv. 2017. A context-enriched neural net- work method for recognizing lexical entailment. In Proceedings of AAAI, volume 31.
Yinhan Liu, Myle Out, Naman Goyal, Jingfei Du, Man- dar Joshi, Danqi Chen, Omer Levy, Mike Lewis. Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: robustly optimized bert pretraining ap- proach. arXiv preprint arXiv: 1907.1 1692.
Kun Zhang, Le Wu, Guangyi Lv, Meng Wang, Enhong Chen, and Shulan Ruan. 2021. Making the relation matters: Relation of relation learning network for sentence semantic matching. In Proceedings of AAAI, volume 35, pages 44411-14419
Boer Lyu, Lu Chen, Su Zhu, and Kai Yu. 2021. Let: Linguistic knowledge enhanced graph transformer for chinese short text matching. In Proceedings of the AAAI, volume 35, pages 13498-13506.


Reviews:
[{"review_id": "bOaE0kWJfE", "reviewer": "Reviewer_rB1x", "summary": "", "questions": "", "limitations": "", "rating": 4, "confidence": 3, "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": "We would like to express our heartfelt appreciation for dedicating your valuable time to reviewing our paper and offering us invaluable insights and suggestions. We have taken careful consideration of each of your suggestions, and we hereby provide our responses to each point as below.\n\n\n\n**----------Response to Comments----------**\n\n\n**Comment #1:** The soundness of mimicking the human learning process should be further discussed in detail. What about multi-turn rethinking or correction for fine-grained errors？\n\n\n**Response to #1:** We elaborate on our motivation in Lines 89-98 and subsequently validate our hypotheses through experimentation, wherein we set different boosting timings and ratios, as detailed in Section 4.5. The concept of multi-turn rethinking or correction as you suggested, is indeed an advisable approach to consider. We didn’t employ multi-turn correction indeed, due to the considerations of time efficiency.  In the future, we will investigate the method you have referenced, while ensuring efficiency as a foundational consideration.\n\n\n**Comment #2:** More error ratio information and the corresponding examples should be analysed to support the motivation of the idea.\n\n\n**Response to #2:** Due to the page limit, we didn’t include error rates and examples in the paper. We evaluated on the QQP verification set, using a Levenshtein similarity with a 0.6 threshold to determine literal similarity (sentences with a similarity greater than 0.6 were considered literally similar). Among the incorrect predictions made by the baseline model, we found that 35.9% were literally similar but not semantically similar (IBH0), and 14.7% were not literally similar but semantically similar (IBH1). These two categories constituted **50.7%** of all baseline model errors. In future revisions, we plan to provide a more detailed error analysis.\n\n\nWe extend our heartfelt gratitude for your insightful comments, which will help us improve the quality of our 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. The method seems promising in term of the efficiency, which is superior to the adversarial methods in this view.\n2. The paper is well written and easy to follow.\n3. All the source codes will be made publicly available to support reproducible research.", "reasons_to_reject": "1. The soundness of mimicking the human learning process should be further discussed in detail.  What about multi-turn rethinking or correction for fine-grained errors？\n2. More error ratio information and the corresponding examples should be analysed to support the motivation of the idea.", "questions_to_authors": "", "paper_topic_and_main_contributions": "The authors proposed a generative boosting training approach for paraphrase identification, which uses seq2seq model to perform data augmentation on misclassified instances periodically. It shows that a single BERT model can outperform the state-of-the-art PI models with the boosting of the proposed approach. Experiments on the benchmarks of English QQP and Chinese LCQMC show that the method improves various PLM models.", "excitement": 4, "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": {}}, {"review_id": "6ULV8ILyyT", "reviewer": "Reviewer_Zs1Z", "summary": "", "questions": "A. Why did you use LCQMC and QQP as metrics?  \nB. Considering BLEU is not a perfect metric, have you thought of using other metrics to quantify the differences?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We extend our sincere gratitude for dedicating your valuable time to reviewing our paper and providing us with invaluable insights and suggestions. Your feedback is greatly appreciated and has undoubtedly contributed to the refinement of our work. We have meticulously considered each of your suggestions and comments. We answer the concerns as below:\n\n\n\n**----------Response to Comments----------**\n\n\n**Comment #1:** The advanced data augmentation methods in comparison are from 2018-2020. It is unclear that whether the paper is comparing with the stat-of-the-art counterparts.\n\n\n**Response to #1:** We totally agree with your opinion. Numerous data augmentation methods exist across various tasks. However, our objective is to generate two distinct categories of challenging samples, including the ones that exhibit literal similarity but possess different semantics (IBH0), and the cases which lack literal similarity while sharing same semantics (IBH1). To the best of our knowledge, there is a limited availability of open-source data augmentation methods of this nature. The data augmentation methods that we have compared are widely employed and adept at generating the required data.\n\n\n**Comment #2:** It is not sure that whether part of the performance improvement can be attributed to the learned language ability from the seq2seq model via generated data (like the language model distillation), but not the GBT.\n\n\n**Response to #2:** Regarding this concern, we conducted an ablation experiment in Section 4.4 and the results can be found in Table 5, where we compare the performance of the baseline model with the ' + Only Boosting Generator' variant. When we train the model solely on data generated by the seq2seq model without using boosting training, we observe a significant drop in model performance. This demonstrates that relying solely on a seq2seq model is insufficient.\n\n\n\n**----------Response to Questions----------**\n\n\n**Question #1:** Why did you use LCQMC and QQP as metrics?\n\n\n**Response to #1:** We chose to use these two datasets primarily because they are well-known benchmarks in paraphrase identification, and a large amount of prior studies has been conducted on them [1][2]. Our experiments on these two benchmarks provide better comparability with previous work. The utilization of ACC and F1 as evaluation metrics also follows established practices in the previous work. We provided an introduction to both of these datasets and the metrics used in Section 4.1.\n\n\n**Question #2:** Considering BLEU is not a perfect metric, have you thought of using other metrics to quantify the differences?\n\n\n**Response to #2:** Indeed, there are several metrics, such as BERTScore [3], that are considered better than BLEU for measuring the similarity between two sentences. However, our specific objective revolves around the identification of **literal similarity** or dissimilarity within sentences. In this context, the BLEU metric effectively fulfills our intended purpose, offering the added advantage of computational simplicity. In our future work, we plan to investigate the impact of different evaluation metrics on our approach.\n\n\n**----------Response to Typos Grammar Style And Presentation Improvements----------**\n\n\nWe sincerely appreciate your valuable feedback on our writing, and we are committed to incorporating your suggestions into the revised version of the paper to enhance its overall quality.\n\n\n\nThank you again for your insightful feedback, which undoubtedly contributes to enhancing the quality and enriching the final version of the paper.\n\n\n\n**----------Reference----------**\n\n\n[1] Boer Lyu, Lu Chen, Su Zhu, and Kai Yu. 2021. Let: Linguistic knowledge enhanced graph transformer for chinese short text matching. In Proceedings of the AAAI, volume 35, pages 13498–13506.\n\n[2] Qiwei Peng, David Weir, Julie Weeds, Yekun Chai. 2022. Predicate-argument based bi-encoder for paraphrase identification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, volume 1, pages 5579-5589.\n\n[3] Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. arXiv preprint arXiv:1904.09675.", "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. Good motivation to solve the paraphrase identification, with current limitations, detailed examples and vivid diagrams\n2. Solid experiment on both BERT and GPT, reaching good performance or even state-of-the-art compared to the baseline models.\n3. Explore the detailed of such data augmented boosting training, including but no limited to, boosting time, boosting ratio, different language models, etc. \n4. GBT is efficient since it can be completed 1-hour on GPU.", "reasons_to_reject": "1. The advanced data augmentation methods in comparison are from 2018-2020. It is unclear that whether the paper is comparing with the stat-of-the-art counterparts.\n2. It is not sure that whether part of the performance improvement can be attributed to the learned language ability from the seq2seq model via generated data (like the language model distillation), but not the GBT.", "questions_to_authors": "A. Why did you use LCQMC and QQP as metrics?  \nB. Considering BLEU is not a perfect metric, have you thought of using other metrics to quantify the differences?", "paper_topic_and_main_contributions": "This paper introduces a generative boosting training (GBT) method to address the paraphrase identification (PI) task. In detail, the authors use seq2seq model e.g. BART, to train two data augmentators for paraphrase ad non-paraphrase respectively. Then, these two types of data are augmented when having wrong case during normal training and thus are utilized to retrain the model.", "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. Figure 1: the English translation misses the red color.\n2. The explanation of BLEU score is needed.\n3. Good to have a diagram to demonstrate the whole pipeline and put in page 1 or 2."}, "extra_text": {}}, {"review_id": "MXsfdqQbzx", "reviewer": "Reviewer_xb7T", "summary": "", "questions": "1)\tHow much was the benefit from just doing DA with IBH1 instances?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We extend our sincere appreciation for your dedicated effort in reviewing our papers and offering valuable suggestions to enhance their quality. Your feedback holds significant value for us, and we genuinely consider each suggestion you provide. We take your feedback seriously, and our response is as follows:\n\n\n\n**----------Response to Comments----------**\n\n\n**Comment #1:** There are potentially numerous baselines as data augmentation for hard examples has several work. Given the closeness with this proposed work of using paraphrases (both negative and positive), some of baselines are necessary for comparison with GBT, especially counterfactual data-augmentation techniques as GBT uses (negative paraphrases in DA i.e., IBH0) Feng et.al., A Survey of Data Augmentation Approaches for NLP Li et.al., Data Augmentation Approaches in Natural Language Processing: A Survey\n\n\n**Response to #1:** Thank you for your valuable feedback. The data augmentation methods you mentioned are indeed valuable references. For comparison, the methods we used as baselines are open-source and readily applicable to the PI task. We are also open to incorporating other techniques, such as using LLMs for data augmentation. We will consider including them in our future experiments.\n\n\n**Comment #2:** Some other, even more simpler baselines could be lower learning rate and training for more number of epochs. This would even strengthen the claims of GBT if improvements are significant.\n\n\n**Response to #2:** Your suggestion presents a valuable comparative baseline. However, in our study, we have opted for the same learning rate and number of training epochs as the previous work and baseline models, so as to ensure a fair comparison. We appreciate your feedback and will include relevant experiments in our future work.\n\n\n**Comment #3:** The ablation study in table 5 seemed more like good baselines which is good to have as it shows GBT is more effective when applied to only hard examples. A better ablation study could be giving just positive paraphrases (IBH1) and just negative paraphrases (IBH0)\n\n\n**Response to #3:** Your suggestion is insightful. In our ablation study, we treated the combination of the two major components of our method (boosting generator and boosting training method) as a unified model first for comprehensive performance verification, and then investigate their respective contributions independently. If space permits, we will discuss the impact of different class data proportions on the experimental results in a separate section, as you have suggested.\n\n\n**Comment #4:** Some of the important technical details are unclear. For example, which datasets and how was the paraphraser trained to generate candidate sentences for selecting IBH0 and IBH1. In line 268-277, more details would be needed as to how and where the 50K examples were selected from.\n\n\n**Response to #4:** In line with your valuable feedback, we appreciate the opportunity to clarify some of the technical details. In our work, as mentioned in line 257, we utilized the training data from paraphrase identification, which corresponds to the training set of datasets LCQMC or QQP used in this work, for training the generator. \n\nRegarding the selection of the 50k examples, we follow a specific criterion, as explained in lines 265-270 and line 274-277. For paraphrased sentence pairs, we randomly sampled 50k pairs from the top 50% with the highest BLEU scores. Conversely, for non-paraphrased sentence pairs, we randomly selected 50k pairs from the top 50% with the lowest BLEU scores. This data selection process yields two distinct sets of examples, aligning with the two characteristics we previously mentioned. These two sets of data were then used to train two separate generators, with the training process elaborated upon in lines 278-288.\n\n\n**Comment #5:** The text in line 293-295 makes the above point a little bit more unclear. It would be difficult for readers to understand and evaluate – “we manually observed the generated examples and find the results acceptable.”\n\n\n**Response to #5:** We conducted a manual evaluation of the generator's performance, which, due to page limit, was not included in the paper. Specifically, we randomly sampled 500 data instances generated by the generator and subjected them to human assessment. For positive samples, our evaluation criterion was that two sentences were lexically similar (differing only in a few words) but semantically dissimilar. Conversely, for negative samples, we evaluated instances where the sentences were lexically dissimilar (with only a few shared words) but semantically similar. The proportions of qualified positive and negative examples are shown in the following table. They demonstrate that the generator's performance aligns well with our requirements. Notably, the accuracy of the negative sample generator is relatively lower, primarily because this task poses a higher level of difficulty, requiring the model to generate sentences that are lexically similar but semantically dissimilar. Exemplar can be found in Table 7 in the submission.\n\n|                    | Positive Pairs | Negative Pairs | All Pairs |\n| ------------------ | -------------- | -------------- | --------- |\n| Qualified          | 228            | 212            | 440         |\n| Total Number       | 250            | 250            | 500       |\n| Qualification Rate | 91.2%          | 84.8%          | 88%       |\n\n\n**Comment #6:** A very minute point – it may be interesting to compare with openLLM methods like LLaMa (after some instruction tuning for PI task).\n\n\n**Response to #6:** This is a valuable suggestion. The reason we didn’t conduct this experiment was to make a direct comparison with the existing studies. The openLLM methods, such as LLaMa, indeed present an interesting avenue for additional investigation. We will carry out the experiments for comparison in the extended version of our paper.\n\n\n\n**----------Response to Questions----------**\n\n\n**Question #1:** How much was the benefit from just doing DA with IBH1 instances?\n\n\n**Response to #1:** When performing DA exclusively on IBH1 instances, our proposed GBT method achieved an accuracy of 89.0% and an F1 score of 89.1% on LCQMC dataset. This performance slightly decreased compared to augmenting both IBH1 and IBH0 data, where the accuracy was 89.2%, and the F1 score was 89.3%. This underscores the necessity of augmenting both classes of data, as it enables the model to learn from a broader set of challenging samples. Furthermore, augmenting only IBH1 instances outperformed the baseline with no data augmentation, which has an accuracy of 88.2% and an F1 score of 88.6%. This highlights the significance of extending learning to augmented data, thereby enhancing the model's performance on unseen data.\n\n\n\n**----------Response to Typos Grammar Style And Presentation Improvements----------**\n\n\nWe sincerely appreciate your valuable feedback on our writing, and we are committed to incorporating your suggestions into the revised version of the paper to enhance its overall quality.\n\n\nThank you again for your insightful feedback, which undoubtedly contributes to enhancing the quality and enriching the final version of the 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)\tThe GBT technique shows improvement (as per the result tables 3 and 4) and could be applied generally to other tasks as well. The negative and positive paraphrase augmentation seems like a good mix of balanced data augmentation (though we don’t know how much just adding IBH1 and just adding IBH0 independently contribute to improvement).\n2)\tThe propose technique seems like a good choice for the PI task and some of the analyses (Figure 2 for boosting interval and ratio, table 5 for why to use GBT for hard examples) highlight interesting takeaways about GBT. ", "reasons_to_reject": "1)\tThere are potentially numerous baselines as data augmentation for hard examples has several work. Given the closeness with this proposed work of using paraphrases (both negative and positive), some of baselines are necessary for comparison with GBT, especially counterfactual data-augmentation techniques as GBT uses (negative paraphrases in DA i.e., IBH0)\nFeng et.al., A Survey of Data Augmentation Approaches for NLP \nLi  et.al., Data Augmentation Approaches in Natural Language Processing: A Survey\n2)\tSome other, even more simpler baselines could be lower learning rate and training for more number of epochs. This would even strengthen the claims of GBT if improvements are significant. \n3)\tThe ablation study in table 5 seemed more like good baselines which is good to have as it shows GBT is more effective when applied to only hard examples. A better ablation study could be giving just positive paraphrases (IBH1) and just negative paraphrases (IBH0)\n4)\tSome of the important technical details are unclear. For example, which datasets and how was the paraphraser trained to generate candidate sentences for selecting IBH0 and IBH1. In line 268-277, more details would be needed as to how and where the 50K examples were selected from. \n5)\tThe text in line 293-295 makes the above point a little bit more unclear. It would be difficult for readers to understand and evaluate – “we manually observed the generated examples and find the results acceptable.” \n6)\tA very minute point – it may be interesting to compare with openLLM methods like LLaMa (after some instruction tuning for PI  task).  ", "questions_to_authors": "1)\tHow much was the benefit from just doing DA with IBH1 instances?", "paper_topic_and_main_contributions": "The work proposes a technique named Generative Boosting Training (GBT) where additional synthetic data is generated for difficult cases (where model makes mistake in prediction) on the task of paraphrase identification (PI). Authors propose to augment paraphrase sentences that are different in text but have similar semantic meaning (referred to as IBH1) and sentences that have similar text but different semantic meaning (referred to as IBH0). The work shows improvement from on 2 PI datasets, PPQ and LCQMC.  ", "excitement": 2, "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": {"Missing_References": "There are a few missing citations throughout the draft. Some of them are as follows:\n1) text in line 92-98\n2) In line 299-300\n", "Typos_Grammar_Style_and_Presentation_Improvements": "1)\tTable 4 uses the same symbols as table 3. It could be useful to add a note to see table 3 caption for symbol meanings"}, "extra_text": {}}]

Author rebuttal:

Meta-review: As a reviewer indicates The work proposes a technique named Generative Boosting Training (GBT) where additional synthetic data is generated for difficult cases (where model makes mistake in prediction) on the task of paraphrase identification (PI). Authors propose to augment paraphrase sentences that are different in text but have similar semantic meaning (referred to as IBH1) and sentences that have similar text but different semantic meaning (referred to as IBH0). The work shows improvement from on 2 PI datasets, PPQ and LCQMC.


The main reasons to accept the paper are the following ones:

	The paper is well written and easy to follow.
	All the source codes will be made publicly available to support reproducible research.
   	The method seems promising in term of the efficiency, which is superior to the adversarial methods in this view.
    	 Good motivation to solve the paraphrase identification, with current limitations, detailed examples and vivid diagrams
    	Solid experiment on both BERT and GPT, reaching good performance or even state-of-the-art compared to the baseline models.
    	Explore the detailed of such data augmented boosting training, including but no limited to, boosting time, boosting ratio, different language models, etc.
    	GBT is efficient since it can be completed 1-hour on GPU.
	The GBT technique shows improvementand could be applied generally to other tasks as well. The negative and positive paraphrase augmentation seems like a good mix of balanced data augmentation (though we don’t know how much just adding IBH1 and just adding IBH0 independently contribute to improvement). The propose technique seems like a good choice for the PI task and some of the analyses (Figure 2 for boosting interval and ratio, table 5 for why to use GBT for hard examples) highlight interesting takeaways about GBT.


The main reasons to reject the paper are the following ones:

   	 The soundness of mimicking the human learning process should be further discussed in detail. 
   	 More error ratio information and the corresponding examples should be analysed to support the motivation of the idea.
	The advanced data augmentation methods in comparison are from 2018-2020. It is unclear that whether the paper is comparing with the stat-of-the-art counterparts.
	It is not sure that whether part of the performance improvement can be attributed to the learned language ability from the seq2seq model via generated data (like the language model distillation), but not the GBT.
	 There are potentially numerous baselines as data augmentation for hard examples has several work. Given the closeness with this proposed work of using paraphrases (both negative and positive), some of baselines are necessary for comparison with GBT, especially counterfactual data-augmentation techniques as GBT uses (negative paraphrases in DA i.e., IBH0) 
    	Some other, even more simpler baselines could be lower learning rate and training for more number of epochs. This would even strengthen the claims of GBT if improvements are significant.
   	 The ablation study in table 5 seemed more like good baselines which is good to have as it shows GBT is more effective when applied to only hard examples. A better ablation study could be giving just positive paraphrases (IBH1) and just negative paraphrases (IBH0)
    	Some of the important technical details are unclear. For example, which datasets and how was the paraphraser trained to generate candidate sentences for selecting IBH0 and IBH1. In line 268-277, more details would be needed as to how and where the 50K examples were selected from.
    	The text in line 293-295 makes the above point a little bit more unclear. It would be difficult for readers to understand and evaluate – “we manually observed the generated examples and find the results acceptable.”
   

Two reviewers are quite positive about the paper, one reviewer less, even after reading the answers provided by authors. 

In sum,  despite the reasons to reject, the paper is well written, the results are reproducible, the techniques are interesting and the methods show improvements. Some clarifications should added if finally accepted.
Decision: Accept-Findings