Quality Estimation-Assisted Automatic Post-Editing
Authors: Sourabh Dattatray Deoghare
Diptesh Kanojia
Fred Blain
Tharindu Ranasinghe
Pushpak Bhattacharyya
Venue: EMNLP 2023 Findings (2023)
Keywords: Automaric Post-Editing
Quality Estimation
Multi-task Learning
TL;DR: The paper compares existing QE-APE combination strategies with a proposed multi-task learning based QE-APE combination strategy that outperfoms the exisitng ones.

Abstract: Automatic Post-Editing (APE) systems are prone to over-correction of the Machine Translation (MT) outputs. While Word-level Quality Estimation (QE) system can provide a way to curtail the over-correction, a significant performance gain has not been observed thus far by utilizing existing APE and QE combination strategies. In this paper, we propose joint training of a model on APE and QE tasks to improve the APE. Our proposed approach utilizes a multi-task learning (MTL) methodology, which shows significant improvement while treating both tasks as a 'bargaining game' during training. Moreover, we investigate various existing combination strategies and show that our approach achieves state-of-the-art performance for a 'distant' language pair, viz., English-Marathi. We observe an improvement of 1.09 TER and 1.37 BLEU points over a baseline QE-Unassisted APE system for English-Marathi, while also observing 0.46 TER and 0.62 BLEU points for English-German. Further, we discuss the results qualitatively and show how our approach helps reduce over-correction, thereby improving the APE performance. We also observe that the degree of integration between QE and APE directly correlates with the APE performance gain. We release our code and models publicly.

Quality Estimation-Assisted Automatic Post-Editing
Sourabh Deoghare', Diptesh Kanojial, Tharindu Ranasinghe',
Frédéric Blain* and Pushpak Bhattacharyya'
'CFILT, Indian Institute of Technology Bombay, Mumbai, India
Suurryy Institute for Crople-Centredd AI, University of Surrey, United Kingdom
Aston University, Birmingham, United Kingdom
"Tilburg University, Tilburg, The Netherlands
(sourabhdeoghare, p iith. ac in, d. kanojia@@urreey ac. uk, t. rwwassinheessttonncoooom ac uk, f.l.g bla.blalnniii. wbr.giiiisttrrrrreeesssttt edu
Abstract
can be costly in terms of time and effort. Quality Estimation (QE) is another auxiliary task that as- sesses the quality of MT output in the absence reference translations (Specia et al., 2020). QE is performed at a word, sentence, and document level. A word-level QE system predicts an OK or BAD tag for each token on both source and target seg- ments, while sentence-level QE assigns rating (0-100), referred as a Direct Assessment (DA) score, the translation denoting its overall quality. Both QE and APE have been explored signifi- cantly as standalone tasks. Over the years, predict- ing MT quality has been formulated differently (e.g. ranking, scoring, qualitative metries) and has been dealt with at diverse levels of granularity. Consis- tent advancements in both these tasks make QE and APE more appealing technologies. Despite the growing interest in the two tasks and their in- tuitive relatedness, previous research in both areas has mostly followed separate paths. Current QE integration strategies in the MT-APE pipeline have only been applied over statistical phrase- baaeed MT system translations (Chatterjee et al., 2018).
Automatic Post-Editing (APE) systems are prone to over-correction of the Machine Trans- lation (MT) outputs. While Word-level Qual- ity Estimation (QB) system can provide a way curtail the over-correttinn, a significant per- formance gain has not been observed thus far by utilizing existing APE and QE combination strategies. This paper proposes joint training a model over QE (sentence- and word-level) and APE tasks improve the APE. Our pro- posed approach utilizes a multi-task learning (MTL) methodology, which shows significant improvement while treating the tasks as 'bar- gaining game' during training. Moreover, we investigate various existing combination strate- gies and show that our approach achieves state- of-the-art performance for a 'distant' language pair, viz. Marrthh. We observe im- provement of 1.09 TER and 1.37 BLEU points over baseline QE- Qn-Usissiseed APE system for English-Marrin Marathi while also observing 0.46 TER and 0.62 BLEU points improvement for Aaglish-German Further, we discuss the re- sults qualitatively and show how approach helps reduce over-correction, thereby improv- ing the APE performance. We also observe that the degree of integration between QE and APE directly correlates with the APE performance gain. We release our code publicly!.
Our motivation for the current work is: the potential usefulness of leveraging the two tech- nologies to develop better APE system has been scarcely explored. Also, there no systematic analysis of APE performance when different QE and APE combination strategies are used to post- edit translations obtained from a Neural Machine Translation (NMT) system. We hypothesize that QE can help APE in the following manner: A sentence- level QE output can provide overall idea of how much editing is required correct translation. whereas a word-level QE output can help APE by identifying translation tokens that need editing. Our goal to reduce over-correction APE by using QE. Hence, our contributions are:
1 Introduction
Significant progress in Machine Translation (MT) and adopting MT systems the translation indus- try has led to motivated research on auxiliary tasks. One auxiliary task in the MT field is Automatic Post-editing (APE), which is aimed toward the au- tomatic identification and correction of translation errors. The APE task is motivated by the need to improve MT output in black-box conditions, where translation models are not accessible for modifica- tion or retraining (Chatterjee et al., 2020), which
Joint training over QE (sentence- and word- level) and APE tasks, which helps APE learn to identify and correct erroneous translation
/https://gtt thub. com/.co//aa /f com/cfiltnll///P

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segments, leading significant (based on the significance test with p value of 0.05) perfor- mance gains for English-Marathi (1.09 TER) and English-German (0.46 TER) over the QE- unassisted APE baseline. (Refer Table 4)
Listimation shared tasks (Kepler al., 2019). These systems utilize an encoder-decoder Recurrent Neu- ral Network (RNN) architecture, commonly called the 'predictor," combined with a bidirectional RNN known as the "estimator," which generates quality estimates. However, one drawback of these ar- chitectures is that they require extensive predictor pre-training, relying on large parallel data and de- manding computational resources (Ive al., 2018). To address this limitation, TransQuest (Ranasinghe et al., 2020b) emerged as solution, winning the WMT20 sentence-level QE (DA prediction) shared task Specia et al. (2020). TransQuest eliminates the need for predictor by leveraging cross-lingual embeddings. The authors fine-tuned an XLM- Roberta model for the sentence-level QE, demon- strating that simple architecture can achieve state- of-the-art results. Subsequently, the TransQuest framework has been extended to the word-level QE task (Ranasinghe et al., 2021). Recently, Deoghare and Bhattacharyya (2022) showed that combining sentence-level and word-level QE systems can help alleviate the problem of inconsistent predictions.
2. A novel approach to multi-task learning (Nash- MTL.) applied to QE and APE, which treats learning both tasks bargaining game. The improvements obtained over the baseline given in the first point.
3. A comprehensive study investigating known QE and APE combination strategies while showing that tighter coupling of both tasks is increasingly beneficial in improving APE performance. For this study, we consider three existing QE and APE combination strategies (QE as APE Activator, QE MT/APE Se- lector, QE as APE Guide) and the proposed method (Joint Training over QE and APE). (Refer Table 5 or Figure 4)
2 Related Work
Martins al. (2017) used APE outputs to im- prove QE systems. Hokamp (2017) used an en- semble of factored NMT models for word-level QE and APE tasks. Chatterjee et al. (2018) com- pared three combination approaches and showed the potential of QE systems in improving APE on output obtained from a phrase-based MT system. We use these three approaches for comparison in the current work. The winning submission of the WMT22 APE shared task shows the usefulness a sentence-level QE system in deciding whether APE has improved translation or not (Bhattacharyya et al., 2022).
During the past decade, there has been tremendous progress in the field of QE and APE, primarily due to the shared tasks organized annually by the Conference on Machine Translation (WMT), since 2012 and 2015, respectively (Zerva et al., 2022; Bhattacharyya et al., 2022).
Läubli al. (2013) and Pal et al. (2016) have shown that the APE systems have the potential to reduce human effort by correcting systematic and repetitive translation errors. Recent APE ap- proaches utilize transfer learning by adapting pre- trained language or translation models to perform post-editing (Lopes et al., 2019; Wei et al., 2020; Sharma et al., 2021). Also, the recent approaches use multilingual or cross- tross-lingual models to get la- tent representations of the source and target sen- tences (Lee al., 2020). Oh et al. (2021) have shown that gradually adapting pre- pre-trained models to APE by using the Curriculum Training Strat- egy (CTS) improves performance. Deoghare and Bhattacharyya (2022) have demonstrated that aug- menting the APE data by perese-level APE triplets improves feature diversity and used the CTS to train the APE system high-quality data.
3 Standalone APE and QE Systems
This section discusses the approaches used to de- velop standalone APE and QE systems. We fol- low the state-of-the-ar approaches for training the systems it allows us to investigate whether the findings of Chatterjee et al. (2018) hold when neu- ral APE and QE systems are used post-edit and assess the quality of NMT-generated translations.
3.1 Automatic Post-Editing
Recently, in the field of QE, neural-based sys- tems such as deepQuest (Ive et al., 2018) and OpenKiwi (Kepler et al., 2019) have consistently outperformed other approaches in WMT Quality
The subsection describes a standalone neural APE system that we use as a baseline as well and refer to it as APE w/o QE

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APE data consists of real edits) and synthetic (artificial post-edits) (Junezys- Dowmunt and Grundkiewicz, 2016). The English- Marathi APE dataset contains 18K real APE triplets from General, News, and Healthcare domains the train set, and the synthetic APE data contains around 2.5M triplets from multiple domains, The train set for the English-German pair contains 7K real APE triplets from the general domain, and the synthetic APE data, eSCAPE (Negri al., 2018), contains around 4M shuffled triplets from multiple domains. We use the corresponding development sets containing IK APE triplets to evaluate the APE systems.
post-
Add & Norm
Foreard
Senc-E
Word-QE
& Norm
Represeriation
Muts Head Altmtion
Add Norm
Add & Norm
Norm
Feed Forward
Feed Forward
Attention
NX
Add
Nx
Add Norm
Add & Norm
XN
Muts-Hladd Attention
Muss-Head Actetion
Masked Mult--eead Allention
Postional Grooding
Passtonnd Encoding
We also use the parallel corpora during the APE training phase. For the English-Maratie pair, use Anuvaad? Samanantar (Ramesh al., 2022), and ILCI (Bansal et al., 2013) datasets containing around 6M sentence pairs. While for the English- German pair, we use the Swws-Commentar--vv 16 WMT22 MT task dataset (Kocmi et al.. 2022) of around 10M sentence pairs.
Inpur Embeeddingg
Input Embeddtings
Output Emberdrings
MT
Figure Dual-encoder dinole-decoder architecture with task-specific heads used for joint training over QE and APE tasks for the English-Marathi Marathi pair. Without com- ponents shown within the dashed rectangle, the diagram depicts the architecture of standalone APE system. Dashed arrows represent tied parameters and common embedding matrices for the encoders and the decoder.
Data Augmentation and Pre-processing We augment the synthetic APE data with automati- cally generated phrase-level APE triplets. First, we train source-to-translation and source-to-post-edd phrase-based statistical MT systems by employ- ing Moses (Koehn al., 2007). The phrase pairs from both MT systems step. Then, we form the APE triplet by matching the source side of both phrase pairs, We control the quality of synthetic APE triplets (including the phrase-level APE triplets) by performing LaBSE- based filtering (Feng et al., 2022) and filter the low-quality triplets from the synthetic APE data. It is done by computing cosine similarity between the normalized embeddings of source sentence and its corresponding post-edited translation. We re- tain the triplet if the cosine similarity more than 0.91. We get around 50K and 60K phrase-level triplets for English- Marathi and English-German pairs, respectively.
We develop the APE system us- ing transformer-basee encoder-decoder architec- ture. We use two separate encoders for the English- Marathi APE system to encode source sentence and its translation, these languages do not share script or vocabulary. Outputs of both encoders are passed to two consecutive cross-attention lay- ers in the decoder. An architecture shown in Fig- ure without the Sentence-QP QE and Word-QE heads represents the English-Marathi APE architecture. While for the English-German APE system, we use a single-encoder single-decoder architecture as there is script and vocabulary overlap between these two languages. Therefore, single encoder encodes the concatenation of source and translation generated by adding '<SEP>' tag between them, and the encoder output is passed to single cross- attention layer in the decoder. For both the pairs, the encoders are initialized using IndicBERT (Kak- wani et al., 2020) weights.
Architecture
extracted in the next
Model Training We follow the Curriculum Training Strategy (CTS) similar to Oh al. (2021) for training our APE systems. It involves gradually adapting model to more and more complex tasks. The steps of the CTS are described below.
Dataset We datasets released through the WMT21 (Akhbardeh al., 2021) and WMT22 (Bhattacharyya et al., 2022) English- German and English-Marathi APE shared tasks, respectively, to conduct the experiments. The
In the first step, we train a single-encoder single- decoder model for performing source-to- to-tgget lan-
2 Anuvaad Parallel Corpus

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guage translation using the parallel corpus. In the next step, we add another encoder to the encoder- decoder model for English-Marathi APE while we use the same architecture for the English-German APE. We train the resulting model for the APE task using the synthetic APE data in the two phases for English-Marathe and in one phase for English- German. In the first phase, we train the model for the APE task using the out-of-domain APE triplets (i.e. any domains except the General, News, and Healthcare for English- Marathi). In the second phase, we train the model using the in-domain syn- APE data is of general (news or wiki) domain, we train the English-German APE model in single phase using all synthetic data. Finally, we fine-tune the APE model using in-domain real APE data. Equa- tion 1 shows the cross-entropy loss function used train the APE model.
The WMT21 QE shared task data for English- German sentence- and word-level QE includes 7K and IK instances in the train and development sets, respectively. For English-Maratie the WMT22 dataset contains 26K and 1K instances in the train and development sets. For evaluating the QE sys- tems, we use the corresponding development sets. Each sample in the word-level English-German QE data consists of source sentence, its transla- tion, and sequence of tags for tokens and gaps. The WMT22 dataset does not contain tags for gaps between translation tokens for the English-Marathi pair. So, we used the CE-corpus DE-corpus-builder'' builder' to obtain annotations for translations using their post-edits.
thetic APE triplets. As the English-
Training Approach We train XLM-R R-based Sent-QE and Word-QE models for each language pair using the respective sentence- and word-level QE task datasets. During training, the weights of all layers of the model are updated.
|S
M Σ
Sentence-level Quality Estimation Head This task is modeled as regression task. We use the hidden representation of the classification token (CLS) of the transformer model to predict normal- ized DA scores (Zerva et al., 2022) through the application of linear transformation:
APE = Σ
swocc log (froc)
w=1e=1
Where |S| and [V] denote the number of tokens in the sentence and the number of tokens in the vo- cabulary, respectively. The APE output is denoted by the iw,e. while Vu,e represents the ground truth.
Jdh = (CLS) MiCLs) + b.CLs
(2)
3.2 Quality Estimation
where - denotes matrix multiplication, W(CLS) E R Dx1, b[CLs] E lx and D is the dimension of input layer h (top-most layer of the transformer). Equation shows the Mean Squared Error (MSE) loss used for this task.
This section describes the standalone sentence- (Sent-QE) and word-level (Word-QF) QE systems.
Architecture We use transformer encoder to develop the QE models. To obtain representa- tions of the input (concatenated source sentence and its translation), we employ XLM-R (Conneau massive
L MSF (yde: Ida
(3)
et al., 2020). This model is trained multilingual dataset of 2.5TB, which includes 104 different languages, and the training is conducted using the masked language modeling (MLM) ob- jective, similar to RoBERTa (Liu et al., 2019). The WMT20 sentence- and word-level QE shared task winning systems have utilized XLM-R-based QE models (Ranasinghe et al., 2020a; Lec, 2020). Therefore, we adopt similar strategy for both QE tasks. We add feedforward layer the top of XLM-R to perform regression (sentence-level QE) and token-level classification (word-level QE).
Word-level Quality Estimation Head We treat this task as a token-level classification task. We pre- dict the word-level labels (OK/BAD) by applying linear transformation (also followed by the soft- max) over every input token from the last hidden layer of the XLM-R model:
Dword (WT word hy bword) (4) where marks which token the model is label within a T-length window/token sequence, W Worrd E Dx2 and bword C R1 The cross- entropy loss utilized for training the model is de- picted in Equation 5, which bears similarity to
Dataset For the QE tasks as well, we use datasets released through the WMT21 (Specia et al., 2021) and WMT22 (Zerva et al., 2022) QE shared tasks.
itttps://gt /ttt::////ttttt thub. wttphhu....coeepeeeeeeeeeee sccm/depp-pptn/ qe corpus-buill

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the architecture of MicroTrans as described in Ranasinghe et al. (2021).
<DA Score <TER SecRRSSSoree <Souree_sentence>> sentence >. Similarly, to pass the word- level QE information, we add a <BAD > token before every source and translation token for which a BAD tag is predicted.
sentence
Eword Σ
Yword 4 log((rnord))
(5)
Joint Training over QE and APE
Refer Appendix A for the details about the hyper- parameters and the hardware used to conduct all the experiments.
This approach investigates the tightest coupling between the QE tasks (sentence- and word-level QЕ) and APE. We follow exactly the same steps in the CTS (explained in Section 3.1), except the last one, and train the model for the APE task. During the last (fine-tuning) stage of the CTS, we jointly train the model on the APE and QE tasks using the real APE training data and the QE training data. To do so, add the task-specific heads (refer Section 3.2) on top of a shared representation layer that receives inputs from the final encoder layers (Refer Figure 1). The representation layer has 2x neurons for En-Mr than for En-De. For En- De, its size is equal to the size of the final encoder layer. The following two multi-task learning (MTL) approaches are used to perform the experiments.
4 Progressively Integrating QE with APE
This section discusses three existing strategies for combining QE with APE. Starting with lighter com- bination approaches, we move towards strongly coupled ones that help APE learn from the QE subtasks (sentence- and word-level QE).
4.1 QE APE Activator
this strategy, we use a QE system to decide whether a translation requires post-editing. We pass the source sentence and its translation to sentence- level QE system to get DA score prediction. If the DA score is below decided threshold', only then do we use the APE system generate the post-edited version of the translation.
Linear Scalarization (LS-MTL) We use a straightforward MTL approach, LS-MTL, com- bine the task-specific losses.
4.2
QE as MT/APE Selector
Contrary to the QE as APE Activator approach, we use a QE system to decide whether an APE output is improved version of translation not. This is done using sentence-level QE system to get DA score predictions for the original translation and corresponding APE output. We consider the APE output as the final output only it receives a higher DA score than the original translation.
518-MTT. Lusett Loword + L LAPE (6)
All loss functions are weighed equally and are added together to get the combined loss (LLS MTL) as shown in Equation 6.
Nash-MTL To further explore the use of so- phisticated MTL methods for training the APE model, we choose the Nash-MTL approach pro- posed by Navon et al. (2022), as the authors have shown that their proposed approach outperforms several other MTL methods through experiments on two different sets of tasks.
4.3
QE as APE Guide
In the earlier two approaches, QE information is not passed to APE, but a sentence-level QE sys- tem is utilized merely to decide whether to APE or consider the APE output. Through the current approach, we explore a tighter combination of QE and APE by passing the sentence-level and word- level information obtained from the QE systems as additional inputs APE. We use sentence-level QE systems predict either DA scores TER (Snover al., 2006) scores. If we pass just the predicted DA or TER score APE, we prepend it. If both scores are to be passed, then we modify the input format:
The utilization of MTL to jointly train a single model has been recognized as a means to reduce computation costs. However, conflicts arising from gradients of different tasks often lead to inferior performance of the jointly trained model compared to individual single-task models. To address this challenge. a widely used technique involves com- bining per-task gradients into a unified update di- rection using a heuristic. In this approach, the tasks negotiate for a joint direction parameter update.
*decided empirically (Chatterjec et al., 2018).

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Algorithm 1 Nash-MTL
En-Mr
En-De
Approach
TER
BLEU
TER
BLEU
Input: 0o - initial parameter vector, (4) differentiable loss functions, 77 learning rate
Do-Nothing
22.93
64.51
19.06
68.79
APE w/o QE
19.39
68.35
18.91
68.91
Output: 0²
APE -SQE
19.01
68.74
18.84
69.00
for t = 1,.... T do
Compute task gradients of V Vo(-1144
Table 2: Results on the English-Marane Marathi WMT22 and Anglish-German WMT21 development sets when QE used as MT/APE Selector.
Set G the matrix with columns 9
(1)
Solve for (G*) 1/a obtain
(!)
En-Mr
En-De
Update the parameters o()) 000) a))0)
Approach
TER
BLEU
TER
BLEU
end for
Do-Nothing
22.93
64.51
19.06
68.79
return 0z
APE w/o QE
19.39
68.35
18.91
68.91
DA
19.32
68.41
19.00
69.02
+ HTER
19.40
68.38
19.03
69.05
The Nash-MTL approach considers a spherical region centered at the origin with a radius of for the MTL problem having parameters 0. The update vectors are constrained within this sphere. bargaining problem,
+ DA HTER
19.35
68.47
19.01
69.01
+ BAD TAGS
18.81
69.12
18.86
68.98
+DA + BAD TAGS
18,86
69.05
18.84
69.02
Table 3: Results on the English- WMT22 and Germmn WMT21 development sets when QE model outputs are passed as additional inputs to APE. +DA, +HTER: sentence-level QE (DA) model predictions and sentence-level QE (HTER) predic- tions passed as additional inputs to APE, respectively; +BAD TAGS: APE input tokens annotated using the word-level QE predictions.
The problem is formulated where the center of the sphere represents the point disagreement and the region serves as an agree- ment set. Each player's utility function is the dot product between the gradient vector gi of task i's loss 1; at 0 and the update vector. Navon et al. (2022) showed that Nash bargaining solution 1/a. Algorithm
is solution (G*) shows the process followed in the Nash-MTL method to update the parameters.
MT system. (ii) A standalone APE system that is unassisted by a QE system (APE w/o QE).
Experiments for the first two combination strate- gies (QE APE activator and QE as MT/APE selector) are performed using the standalone APE and QE systems. For the QE as APE Activator ex- periments, we experiment with different thresholds of the DA scores and report the best results. The QE as APE Guide experiments use the same stan- dalone sentence-level and word-level QE systems, but we modify the APE inputs from the second step of the CTS onwards, for each experiment, and train different APE models following the same training approach described in Section
En-Mr
En-De
Approach
TER
BLEU
TER
BLEU
Do-Nothing
22.93
64.51
19,06
68.79 28.79
APE w/o QE
19.39
68.35
18.91
68.91
QE -PAPE
21.04
64.66
20.20
67.53
Table I: Results on the English-Marathi (En-Mr)
WMT22 and English-Germanl (En-De) WMT21 devel- opment sets when QE is used as an APE activator.
6 Results and Analysis
In the MTL--based experiments, first, we use the LS-MTL approach to perform various experiments using combination of tasks to see the improve- ments brought by simple MTL approach. Further, try optimize the performance the best com- bination of tasks through the Nash-MTL approach.
This section discusses an evaluation of the QE and APE combination strategies. The performance of the APE systems is reported on the WMT devel- opment sets in terms of TER (Snover et al., 2006) and BLEU (Papineni et al., 2002) scores. We per- form a statistical significance test considering pri- mary metric (TER) using William's significance test (Graham, 2015).
QE as APE Activator We experimented with various threshold DA scores for each language pair and tried to find the optimal threshold value over a held-out subset taken from the train set. Thresholds of 0.32 and 0.35 DA scores give the best results
For comparison, we use two APE baselines as follows: (i) a Do-Nothing baseline that does not modify original translations, as obtained from the

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for English-Marathi and Enggishh-Geman language pairs, respectively.
pling of Qł and APE. Table 4 compiles MTL-based results. We jointly train a model using LS-MTL in each experiment on a different combination of the APE and QE tasks. We observe better improve- ments by training on the word-level QE task and APE than when combining the sentence-level QE (DA prediction) task and APE. Further addition of the sentence-level QE (HTER prediction) does not significantly improve the performance. Unlike the findings from the QE as APE Guide experimental results, we get better improvements when sentence- level QE (DA prediction) and word-level QE tasks are used along with APE.
The TER and BLEU scores obtained using this approach for both language pairs are reported in Table 1. We see a drop in performance compared to the APE w/o QE baseline system for both language pairs. For English-German we observe that the performance is even poorer than the Do-Nothing baseline, which suggests the possibility that the APE can correct minor errors even the transla- tions that receive a very high DA score, A possible reason for not seeing improvements over the stan- dalone APE system could be that the sentence-level QE information is too coarse for APE to figure out whether a translation requires editing.
As jointly training a model using LS-MTL on the APE, Sent-QE (DA), and Word-QE tasks yields high improvements. we try to improve it using an advanced MTL method. Using the Nash-MTL. method, we get further APE performance enhance- ments of 0.24 and 0.15 TER points for English- Marathi and English-Germat pairs, respectively. Similarly, gains over the APE w/o QE baseline system are 1.09 and 0.46 TER points.
QE as MT/APE Selector Table shows the help- fulness of this combination strategy. We see 0.38 and 0.07 TER point improvements over the APE w/o QE baseline system for English-Marathi and English-German German pairs, respectively. The results highlight the problem of over-correction that the neural APE systems face. We conjecture that the sentence-level QE information may be too coarse to decide whether a translation needs post-editing, Still, it can be used to compare the translations.
Additional analysis, like the efficiency of these combination strategies, the number of translations improved or deteriorated by the jointly trained sys- tems, and types of editing performed by the models, is presented in Appendix B and Appendix C.
QE as APE Guide Table 3 reports the results of experiments when different combinations of the QF information are embedded into the APE inputs. It shows that passing sentence-level QE information to APE is not much effective. A possible reason could be the coarse nature of the sentence-level QE as discussed earlier. In particular, we see better performance when predicted DA scores are passed than the predicted HTER scores, which do not con- sider the semantic meaning.
Key Observation Our quantitative analysis from the comparison between the improvements (Table 5, Figure 4) brought by each combination strategy over the APE w/o QE baseline shows that as the QE and APE coupling gets stronger, we see better enhancements in the APE performance.
Qualitative Analysis We compared the English- Marathi QE-assisted APE outputs (of the Nash- MTLL-based method) with the corresponding APE w/o QE outputs. Our finding that QE helps APE mitigate the over-correction issue is supported by multiple examples from the sample of the data we analyzed. We show two examples in Figure 2.
We observe the best results when the source and translation tokens, tagged as BAD by the word- level QE, are annotated and passed to the APE for the Aaglish-Maratie pair. For the Iaglish-German pair, we get the best results when both the predicted DA scores and the word-level QE predicted infor- mation is passed as additional inputs to APE. With these combinations, we get the 0.58 and 0.07 TER point improvements for the English-Marathi and English-Genne German pairs, respectively. It suggests that granular word-level QE information helps APE identify poorly translated segments, thereby help- ing it focus on their correction.
The first example shows a fluent and highly adequate translation. Unlike the QE-unassisted APE system that modifies the translation and re- places three words, compromising adequacy, the Qs-assitted APE leaves all translation words un- touched, except the 'ghaaloon', which is tagged BAD by the Word-QE, is changed to 'ghaatalelee' which improves the fluency. In the second exam- plc, the APE w/o QE system has compromised the fluency by dropping the pronoun 'he' in the trans-
Joint Training over QE and APE Through these experiments, we explore the most robust cou-

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En-Mr
En-De
Approach
TER
BLEU
TER
BLEU
Do-Nothing
22.93
64.51
19.06
68.79
APE w/o QE
19.39
68.35
18.91
68.91
LS-MTL JAPE, Word-QE]
18.78
69.18
18.62
69.28
LS-MTL [APE, Sent-SEE (DA))
19.52
68.92
18.89
68.90
LS-MTL [APE, Sent-QE (HTER)
19.69
67.66
18.95
68.94
LS-MTL [APE, Word-QE, Sent-QE (DA)
18.54
69.45
18.56
69.37
LS-MTL JAPE, Word-QE, Sent-QE (DA), Sent-QE (HTER)
18.54
69.44
18.60
69.31
Nash-MTL [APE, Word-QE, Sent-QE (DA))
18.30
69.72
18.45
69.53
Table 4: Results the English-Marathe WMT22 and English-German WMT21 development sets when model is jointly trained on the APE and QE tasks.
Example 1
Example 2
Description: store sculpture vishnu's head, wearing high Kirkamukuta.
When the instrument is moved rapicly, ly, the balls hit the stretched skin, and make the rhythmic soundi
Source
shagt it zi ănd goanch and trege dig cenobam geiter arreanet अτी expig aтærо अरयार..
Output
अमन fियपरय ahauns बात gfft, कitllge Bie
Shen ame ánir अеअपc पा,, समरर #i कााअctगाा
Reference
cofi:: Resperr cheund) end vell and dffdgge unsekell.
wieed in at l esuing sexeet extelid.
Sheee ds ेार अभरत sा,,, रoy fig shacken रााडकार siveard ereeet अाााeा (Rfjl बररार..
APE
cofe: Respear eheurel retel self, ah shlddgge unetrr srerkelf
Sunl-QE
0.28
0.11
Word-QE
intrauu meg
Shst
BIRE Riee
APE with QE
anfi: Reuquu sोasाs) avl पहीी. ae ffllagee unactelt.
Shaa اासकरात अा काकार डालााय कहरााा..
रोेा sig shaeerrn
Figure 2: Post-edits from Mogglih-Marathe APE w/o QE and Joint training approach-based APE (APE with QЕ) systems (Refer Figure for gloss and English transliterations of the Marathi sentences). Underlined words show differences between the MT Output and the APE reference. Incorrectly translated words colored in red. Words/space highlighted with green and red represents the predicted OK and BAD tags, respectively. The Sent- QE row shows the predicted normalized DA scores. The Sent-QE and Word- predictions are from the Nash-MTI-hhased jointly trained model.
En-Mr
En-De
see the Qs-sssisted APE corrects the translation of the BAD-tagged word but unaffected by the
Approach
TER
BLEU
TER
BLEU
No-Nothing
22.93
64.51
19.06
68.79
APE w/o QE
19.39
68.35
18.91
68.91
wrongly tagged gap.
QE as APE, Activator
21.04
64.66
20.20
67.53
APE as MT/APE Selector
19.01
68.74
18.84
69.00
Conclusion and Future Work
QE as APE Guide
18.81
69.12
18.84"
69.02
Joint Training
18.30
69,72
18.45
69.53
In this paper, we have investigated four strategies to integrate QE systems into the MT-APE pipeline, intending to improve APE performance when trans- lations be post-edited are obtained from an NMT system. Experimental results highlight the comple- mentary nature of APE and QE tasks, which can be best utilized through our proposed approach: joint training of model that learns both tasks simultanc- ously. The quantitative results and our qualitative analysis suggest that the QE helps APE to address the over-correction problem.
Table 5: Overall results on English- Marathi WMT22 and English-German WMT21 dev sets using different QE and APE integration strategies. [" indi- cates the improvement is not significant.]
lation and by translating the English phrase 'make the rhythmic sound' literally. For this translation, the Sent-QE prediction suggests need for some editing. The Word-QE correctly tags the word "pasaralelyaa' as 'BAD.' It wrongly tags the word 'yantra' as 'OK' and the gap before the last word (suggesting missing words before it) as 'BAD. We
Among the existing combination strategies, the QE as MT/APE Selector improves performance over QE-unassisted APE by discarding low-quality

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APE outputs. APE Guide is more beneficial ap- proach as we observe even better improvements using the tighter combination strategy. It provides APE with explicit information about the overall translation quality and erroneous segments. More- over, a comparison of these combination strategies shows that tighter coupling between QE and APE is increasingly beneficial for improving APE.
References
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We plan conduct a thorough qualitative anal- ysis of QE-assisted APE systems to gain a deeper understanding of the contribution of QE. We also aim to strengthen the coupling between APE and QE to enhance APE performance further. Addition- ally, we intend to conduct a study to evaluate the potential of APE in improving QE systems.
8 Limitations
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In the current work, we focus on using QE to im- prove APE, and we see that the joint training on the QE tasks and APE helps improve APE perfor- mance. This joint training may also benefit QE. However, we have not investigated it in the current work. We observe more minor performance im- provements in the case of the English-German pair, having a strict Do-Nothing baseline, compared to the English-Marathi. It suggests that the law of diminishing returns affects the QE and APE com- bination strategies. Improvements obtained by the QE and APE coupling tend to get lower as the qual- ity of original translations keeps improving. The current work has compared four QE and APE com- bination strategies using only two language pairs due to the unavailability of an adequate amount of good-quality APE and QE datasets for other language pairs. Current findings may be domain- specific, and future research could evaluate the pro- posed combination strategies on diverse domains to assess their generalizability and robustness.
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9 Ethics Statement
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettle- moyer, and Veselin Stoyanov. 2020. Unsupervised lingsllingual representation learning at scale. In Pro- ceedings of the 58th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 8440- 8451, Online. Association for Computational Lin- guistics.
Our APE and QE models are trained on publicly available datasets referenced in this paper. These datasets have been previously collected and anno- tated; no new data collection has been carried out as part of this work. Furthermore, these are standard benchmarks released in recent WMT shared tasks. No user information was present in the datasets, protecting the privacy and identity of users. We understand that every dataset is subject to intrinsic bias and that computational models will inevitably learn biased information from any dataset.
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A Training Details
To maintain uniformity across all the experiments, use identical set of settings for all the QE and APE experiments. Our APE models are trained using batch size of 32. We specified a maximum of 1000 epochs for training, implementing early stopping with patience of 5. The Adam optimizer was employed with a learning rate of 5 x 10 -
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Example
Description:
The stone sculpture Vishnu's head, wearing
Source
high Kiritamukuta.
Varnan (Description): Vishnoochya (Vishnu's)
MT Output
पपन किपपया डोयपातीी अाc पeी,, उस ahtdaaee siet.
dokyaachee head) dagadee (of stone) moortee
(sculjture), uchcha (high) keertimukut (Kirtimukuta) ghaaloon (after wearing).
Reference
पगiन: Reयप डोयपायी साती uef,, अस offiggee nाeei
Varnan: Vishnoochya dokyaachee dagadee moortee, uchcha keertimukut ghaataleine (wom).
Varnan: Vishnoochya dokyaachee dagadee moortee,
APE w/o QE
पाेन: लियपरय stranh endi sffi, अм difilage ueed arसर
uncha (unch) keertimukut ghaalat (being worn) asalelee (being done).
Sent-QE
0.28
Word-QE
पuनी Bue Steuncl cnsl weil ue antdugee slet
Varnan: Vishnoochys dokyaachee dagadee moortee,
uchcha keertimukut ghaaloon.
APE with QE
अगनन: ीीयपरया डोयपररी साती seff, dhfilage पााीयी..
Varnan: Vishnoochya dokyaachee dagadee moortee, uchcha keertimukut ghaatalelce.
Example
When instrument moved rapidly, the balls stretched skin, and make the rhythmic sound.
Source
Jevhaa (wween) he (that) yantra (machine) vegaane (witt speed) halawale (Is moved) jate (done). tevhaa that dime) chendu (balls) pasaralelya (spread) twachewar (on skin) andalataat (hit) aani (and) layabaddha (rhythmiic aavaaj (sound) karataat (make).
MT Output
aloge it dia âvp şerपeh und duge #ig wereseen vιलuरर succeeete smta erern sevess परयाा..
Jevhaa vadya (instrument) vegaane halawale jate. tevhaa chendu pasaralelya chaamadvaawar (on animal
Reference
abrent èt yrer ेलत अभअगरे पपर, रven sig पसारोरगाा anrcemne smraasettt filी errvag smrr meeee..
skir) aapatataat aani layabaddha aavanj karalnat.
yantra (machine) vegaane halawale jaate, tevhaa chengdu odhelelyaa chaamadyaswar sapatateat
APE wlo QE
Gbage vin ेाप अअssरी uार लeyr sig shaekeen मगनाकायार suvecnt smfi अाया sाराssss Pffffe axतात.
(hit) aani layabaddha aavaajaachi (af sound) nimmitee (to create) karataat.
Sent-QE
0.11
Jevhas he vanita vegaane balewale jate, levhas chendu peseralelys taachewar sadalatast aani layabadaha
Word-QE
Sat4e Widd
perte Servcaati
shectiet
aavenj
Jevhaa he yantra vegaane halawale jaate, tevhaa chengdu odhalelyaa chaamadyaawar aadalataat aani layabaddha aavaa) karataat.
APE with QE
लेbा è सiн (eा (अअारe und लसeा sig aitzeheem सामेाागररर
Sाсςाा eारा अपराा परकाा..
Figure 3: Post-edits from English- Maritti APE w/o QE and Joint training approach- approach-based based APE (APE with QE) systems. The third column contains the gloss (shown in the brackets) and transliterations of Marathi sentences. Underlined words show differences between the MT Output and the APE reference. Incorrectly translated words are colored in red. Words/space highlighted with green and red represents the predicted OK and BAD tags, respectively. The Sent-QQ nEt-QQ row shows the predicted normalized DA scores. The Sent-QE and Word-QE predictions are from the Nash-MTI -based jointly trained model.
along with 31 set to 0.9, and 8 set to 0.997. Addi- tionally, we utilized 25,000 warmup steps. On the decoder side, beam search was applied with a beam size 5. For the QE experiments, we use batch size of 16. We start with a learning rate of 2e - and use 5% of training data for warm-up. We use early stopping and patience over 20 steps. In the QE experiments and all MTL--aased experiments, we used WandB for hyperparameter search. All the experiments are performed using NVIDIA A A00 GPUS. The APE model contains around 40M pa- rameters and training the model using CTS takes about 48 hours. The QE model has about 125M pa- rameters and training one QE model takes around
2.25 hours. For pre-processing the English and German data, used the NLTK library",, and the IndieNLP library is used for processing Marathi text. We used Pytorch? for Model training and in- ference. For computing the TER scores, we use the official WMT APE and QE evaluation script and for computing the BLEU scores, we use the SacreBLEU" library.
'https://www nltk. org/
'nttps://githun ccm ccom//nnoookacccuuutteell indic_nlp library
"nttpes//pytorcc.org)
"nttps://github.e www.h/...cm///taao.. htt.h//....caeehffeeeliii....p..
qe scriptsssripts
"https://github. http:///ggttabbccoc/ppp/tttrobat

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Approach
Time (in Seconds)
APE w/o QE
55.70
QE as APE Activator
66.18
QE as MT/APE Selector
109.07
Joint Training
73.96
Edit Operation
En-Mr (%)
En-De (%
Insertion
33.8
18.5
Table 6: Inference time on IK English-Marathi samples of the WMT22 dev set.
Deletion
52.3
63.0
Substitution
7.1
8.25
Shifting
6.9
10.2
En-Mr ( ())
En-De (%)
Modified
43.8
46.2
Table 8: Statistics showing the percentage of insertion, deletion, substituton,, and shifting operations performed
Improved
65.5
45.6
Deteriorated 26.8
38.9
by the APE-QE jointly trained models WMT22 English-Mart Marathi and WMT21 English-Gernen German dev sets.
Precision
71.1
54.0
Table 7: Percentage (%) of translations in the WMT22 English-Marathi and WMT21 English-German dev sets modified, improved, and deteriorated by the APE-QE jointly trained model. The last row reports the preci- sion, computed as ratio the number translations improved the total number of translations modified.
B Efficiency of APE Systems
Table 6 shows the inference time of the four APE- QE coupling strategies for Marathi translations on the same single GPU. The first two QE-APE combination strategies are notably faster than the latter two. The 'QE as APE Guide" strategy shows the highest latency, likely due to word-level tag generation from QE and then longer inputs passed APE. Even though it is pipeline-free free, the fourth strategy exhibits somewhat higher latency than the second strategy; the likely reason could be that involves word-level tag generation. To summarize, strategy choice has a high impact on efficiency. De- spite having slightly higher latency than the 'QE as APE Selector' strategy, the "QE-APE MTL-based approach' remains the optimal choice due to its superior overall performance.
19
En-Mr En-De
C Additional Quantitative Analysis
We analyzed the number of translations improved and deteriorated by the jointly trained APE models on the QE and APE tasks. The results are reported in Table 7. Also, the number of edit operations performed by these jointly trained models are com- piled in Table 8
81
82 M1 M2 142 MM
both language pairs where progression can be seen as start coupling QE and APE tightly. B2: Do-Nothing and QE- Unassissitedd APE baselines; QE as APE Activator; M2: QE as MT/APE Selector; QE APE Guide; M4: Joint Training.
Figure 4: TER (top) and BLEU (bottom)
D Example Post-Edite with English Transliteration
Figure 3 contains English transliterations of the Marathi sentences shown in Figure


Reviews:
[{"review_id": "JNAqsyUakC", "reviewer": "Reviewer_R7xZ", "summary": "", "questions": "\n", "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": "Thank you for reviewing our work and for your valuable comments and suggestions.\n\n****\n\n### Low Scientific Novelty:\n\n**Focus:** The focus of this work is to underline the benefit of using QE systems to improve APE systems when translations are obtained from an NMT system (in a black box setting).\n\n**Novelty:** While the application of multi-task learning in NLP is not new, we believe that the novelty of the work lies in introducing a previously unexplored direction: using multi-task learning to improve APE systems with the help of QE, especially in a jointly trained setting.\n\n**Implications:** We have demonstrated through comparison with the three existing QE-APE combination techniques that the multi-task-learning-based QE-APE combination technique outperforms them. We firmly believe that the implications of this work will not be limited to this immediate finding, but will also act as a foundational work for the APE community to explore this direction further.\n\n### Relatively Small Evaluation (Only Two Language Pairs):\n\nWe conducted a comprehensive evaluation of the APE-QE combination methodologies using the English-Marathi (En-Mr) and English-German (En-De) language pairs.\n\nThese pairs offer distinct settings that encompass various factors:\n- Marathi and German languages belong to different language families.\n- English and German have a script and vocabulary overlap, while English and Marathi have different scripts, and almost no vocabulary overlap.\n- Representing differing resource levels for Machine Translation (MT); where En-Mr stands as a low-resource language pair while En-De is recognized as a high-resource language pair.\n- The APE do-nothing baseline TER score is 22.93 for En-Mr, indicating relative ease, and 19.06 for En-De, signifying a more challenging scenario.\n- The available Synthetic and Real APE data comprises 2.5M and 18K triplets, respectively, for En-Mr, and 4M and 7K triplets, respectively, for En-De. This indicates a relative scarcity of synthetic data yet a significant amount of real APE data for En-Mr, in contrast to the data availability in En-De.\n\n**Given the diversity within these variations, our rationale was to ensure comprehensive coverage by evaluating the experiments across these two language pairs. As the evaluation of two language pairs doesn’t seem enough, we will conduct more experiments on the En-Ru pair and include the additional findings in the appendix**.\n\n### Missing References:\n\nThank you for bringing it to our notice! We will cite these publications in our work.", "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": "- well written paper with good attention given to multi-task learning setup\n- generally interesting multitask learning formulation for jointly training QE and APE", "reasons_to_reject": "- low scientific novelty\n- relatively small evaluation (only two language pairs)\n", "questions_to_authors": "\n", "paper_topic_and_main_contributions": "The authors train a model with both APE and QE data in a multi-task learning (MTL) setting. The authors train a model with both Automatic Post-editing (APE) and quality estimation (QE) data in a multi-task learning (MTL) setting. The work explores four strategies to use QE to augment APE - (1) use QE as a gate to an APE model when the direct assessment (DA) score is below a threshold, (2) use QE to determine whether an APE output is better than the original, (3) provide QE outputs as additional inputs to APE, and (4) train a model using both QE and APE tasks in a MTL setting. On English-Marathi and EN-DE APE tasks, the jointly trained APE model shows some improvement over baselines. ", "excitement": 3, "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": {}, "extra_text": {"Missing_References": "Hokamp 2017 Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation\nSame model is used for APE and QE tasks\n\nMarcin Junczys-Dowmunt and Roman Grundkiewicz. 2016. Log-linear combinations of monolingual and bilingual neural machine translation models for automatic post-editing.\nOriginal synthetic data generation for APE paper\n\n"}}, {"review_id": "WEnRQHH0XH", "reviewer": "Reviewer_WKNZ", "summary": "", "questions": "Question 1:\nthe EN-DE system does not benefit as much as the other one. Do you have ideas why?\n(Might the dual-encoder setting also be beneficial for EN-EN, even though the languages share\nscript and some vocabulary? Is the initialization with IndicBERT optimal also for EN-DE? )\n\nQuestion 2:\nData augmentation with phrase extraction:\nI found this section a bit difficult to understand; if you extract phrases following the Moses routine,\nI would expect the resulting phrases to be rather short, and maybe even not particularly helpful due\nto not enough context (in contrast to the sentence-level triplets from the original data).\nHow many triplets do you get with this extraction step?\n\nQuestion 3:\nYou state that your approach address over-correction (line 77 on page 1):\nthis seems straightforward to me in the QE as Activator and QE as Selector strategies, but it seems more complex in the joint variants. \n\nAlso, did you check whether the number of edits changed in the system variants?\nSimilarly, it would be interesting to know whether there are differences in error types that are addressed by the different system variants. \n", "limitations": "", "rating": 4, "confidence": 2, "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": "Thank you for reviewing our work and providing valuable comments and suggestions.\n\n****\n\n### The experiment is carried out on two language pairs:\n\nWe conducted a comprehensive evaluation of the APE-QE combination methodologies using English-Marathi (En-Mr) and English-German (En-De) language pairs.\n\nThese pairs offer distinct settings that encompass various factors:\n- Marathi and German languages belong to different language families.\n- English and German have a script and vocabulary overlap, while English and Marathi have different scripts and almost no vocabulary overlap.\n- Representing differing resource levels for Machine Translation (MT); where En-Mr stands as a low-resource language while En-De is recognized as a high-resource language.\n- The APE do-nothing baseline TER score is 22.93 for En-Mr, indicating relative ease, and 19.06 for En-De, signifying a more challenging scenario.\n- The available Synthetic and Real APE data comprises 2.5M and 18K triplets, respectively, for En-Mr, and 4M and 7K triplets, respectively, for En-De. This indicates a relative scarcity of synthetic data yet a significant amount of real APE data for En-Mr, in contrast to the data availability in En-De. \n\n**Given the diversity within these variations, our rationale was to ensure comprehensive coverage by evaluating the experiments across these two language pairs. As the evaluation of two language pairs doesn’t seem enough, we will conduct more experiments on the En-Ru pair and include the additional findings in the appendix.**\n\n### Reporting Comet scores:\nWe have reported TER and BLEU scores for the experiments, a convention in the APE community. However, we are happy to report the Comet scores in the paper.\n\n### Question 1:\n**Low improvement for En-De**: The En-De APE Do-nothing baseline (19.06 TER points) is a bit tough, suggesting the presence of many high-quality translations in the test set. While the synthetic data is high in amount, the TER of synthetic data is higher than that of small-sized real APE data that contains 7K triplets.\n\n**Dual-encoder single-decoder for En-De**: Such architecture has been used before, and Correia et. al [1] showed that using a single-encoder single-decoder architecture achieves better performance for the En-De APE.\n\n**Use of IndicBERT for En-De**: IndicBERT-based encoder initialization may not be optimal for En-De. In the current work, our focus was to study whether we can improve APE with the help of QE using multitask-learning and not to achieve the best performance by focusing on other techniques and design of the architecture.\n\n### Question 2:\nWhile the phrase-level triplets are not directly helpful in performing the sentence-level post-editing, we conjecture that they help the model learn the correction of phrases. It has been shown by the winning submission to the last year’s APE shared task. Only the synthetic APE data is augmented with the phrase-level APE triplets, not the real APE data. The number of augmented phrase-level APE triplets for En-Mr and En-De pairs are 50K and 60K, respectively. We will add this discussion to the paper to bring more clarity.\n\n### Question 3, stats about translation edits, analysis based on error types:\nThis observation is based on our qualitative analysis. We did not discuss it in more detail due to space limitations. We will add more qualitative examples with their analysis (including types of errors) and statistics on the number of translations that APE improved and deteriorated in the paper.\n\n****\n**Reference:**\n\n[1] Gonçalo M. Correia and André F. T. Martins. 2019. A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3050–3056, Florence, Italy. Association for Computational Linguistics.", "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": "Previous research in QE and APE has mostly followed separate paths, and the authors combine both under the assumption that QE can help APE. The paper present a novels approach to multi-task learning; comparing different QE and APE strategies, the authors show that a tighter coupling is beneficial.\nThe results are consistent for both language pairs, even though EN-DE improved less.", "reasons_to_reject": "The experiment is carried out on two language pairs, and it would be good to see whether the improvements are consistent for other language pairs as well (Assuming the availability of data sets...)\nAlso, it would be good to have Comet scores in addition to BLEU.", "questions_to_authors": "Question 1:\nthe EN-DE system does not benefit as much as the other one. Do you have ideas why?\n(Might the dual-encoder setting also be beneficial for EN-EN, even though the languages share\nscript and some vocabulary? Is the initialization with IndicBERT optimal also for EN-DE? )\n\nQuestion 2:\nData augmentation with phrase extraction:\nI found this section a bit difficult to understand; if you extract phrases following the Moses routine,\nI would expect the resulting phrases to be rather short, and maybe even not particularly helpful due\nto not enough context (in contrast to the sentence-level triplets from the original data).\nHow many triplets do you get with this extraction step?\n\nQuestion 3:\nYou state that your approach address over-correction (line 77 on page 1):\nthis seems straightforward to me in the QE as Activator and QE as Selector strategies, but it seems more complex in the joint variants. \n\nAlso, did you check whether the number of edits changed in the system variants?\nSimilarly, it would be interesting to know whether there are differences in error types that are addressed by the different system variants. \n", "paper_topic_and_main_contributions": "The paper proposes joint training of a model over QE (at sentence-level and at word-level) and APE tasks to improve Automatic Post Editing. The authors apply a multi-task learning strategy.\n\nThey present a comprehensive study of known QE and APE strategies, showing that a tighter coupling of the two is beneficial.\nThey show an improvement in BLEU and TER for English-Marathi, and to a lesser extent, also for English-German.\n\nHypothesis: QE can help APE\n- sentence-level QE provides overall idea of how much editing is required\n- word-level QE can help by identifying the tokens that need editing\nThe goal is to reduce over correction in APE, which is a common problem.\n\nThe main contributions are a joint training over QE and APE, which leads to improvements on two language pairs, \na novel multi-task strategy, and a comprehensive study of QE and APE combination strategies.\n\n", "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": "HI2tjEnCLF", "reviewer": "Reviewer_aQPt", "summary": "", "questions": "A. Do you have an indication of how different strategies of integrating the two tasks impact efficiency?\n\nB. It was not entirely clear to me which parameters are shared. Figure 1 shows that all of the encoder parameters are tied for both languages, but the caption also mentions the decoder parameters. Could you elaborate?", "limitations": "", "rating": 3, "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": "Thank you for reviewing our work and providing valuable comments and suggestions!\n\n****\n\n### Question A:\n- We interpret this question as how the QE-APE combination strategy impacts the post-edit generation time.\n\n- We measured the post-edit generation time of each of the four strategies for 1K Marathi translations on the same single GPU:\n    1. QE as APE Activator: 53.70 seconds\n    2. QE as APE Selector: 66.18 seconds\n    3. QE as APE Guide: 109.07 seconds\n    4. QE-APE MTL-based Approach: 73.96 seconds.\n\n- The first two QE-APE combination strategies were notably faster than the latter two. The ‘QE as APE Guide’ strategy showed the highest latency, likely due to word-level tag generation from QE and then longer inputs passed to APE. Even though it is pipeline-free, the fourth strategy exhibits somewhat higher latency than the second strategy; again, the likely reason could be that it involves word-level tag generation.\n- To summarize, strategy choice had a high impact on efficiency. Despite having slightly higher latency than the ‘QE as APE Selector’ strategy, the ‘QE-APE MTL-based approach’ remains the optimal choice due to its superior overall performance.\n- We will include these statistics and discussion in the appendix.\n\n### Question B:\nIt is our mistake that the horizontal dotted arrow is not present between the encoder and the decoder in the figure. The caption is correct. We have noted it, and we will update the figure. Thank you for pointing it out.", "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": "The paper is well written and easy to follow. The authors do a great job at introducing the two tasks and motivating why they could benefit from each other. They investigate several ways of combining the tasks, and carry out multiple ablation studies for each one of them. Their results in both tested language directions show significant improvements by using the proposed methodology.", "reasons_to_reject": "I do not see any compelling reason for concern.", "questions_to_authors": "A. Do you have an indication of how different strategies of integrating the two tasks impact efficiency?\n\nB. It was not entirely clear to me which parameters are shared. Figure 1 shows that all of the encoder parameters are tied for both languages, but the caption also mentions the decoder parameters. Could you elaborate?", "paper_topic_and_main_contributions": "This paper addresses the issue of over-correction by Automatic Post-Editing (APE) systems when processing Machine Translation (MT) outputs. In particular, it investigates how Word-level Quality Estimation (QE) systems could be used to limit over-correction, an area where little performance gain has previously been observed. They carry out several experiments to explore the nature of combining APE and QE, and how it affects translation quality. They show that a framework of joint training with Nash - Multitask Learning performs the best, and obtains significant improvements in English-to-Marathi and English-to-German language directions.", "excitement": 3, "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": {}, "extra_text": {}}]

Author rebuttal:

Meta-review: The authors investigate several methods to use a QE (sentence-level and word-level Quality Estimation) system to improve an APE (Automatic Post-Editing) system for machine translation.
They conclude a joint training of a model (dual-encoder single-decoder model with task-specific heads) with Nash-MTL (Mult Task Learning)
performs the best and obtains significant improvements in English-to-Marathi and English-to-German translation tasks.

The paper is well-written, and the experiments show the effectiveness of the proposed method.
However, one reviewer has judged that the proposed method has low scientific novelty because it merely applies a known MTL method to a slight extension of a standard APE model.
Decision: Accept-Findings