Understanding and Improving the Robustness of Terminology Constraints in Neural Machine Translation
Year: 2023

Abstract:
In this work, we study the robustness of two typical terminology translation methods: Placeholder (PH) and Code-Switch (CS), concerning (1) the number of constraints and (2) the target constraint length. We identify that existing terminology constraint test sets, such as IATE, Wiktionary, and TICO, are blind to this issue due to oversimplified constraint settings. To solve it, we create a new challenging test set of English-German, increasing the average constraint count per sentence from 1.1∼1.7 to 6.1 and the length per target constraint from 1.1∼1.2 words to 3.4 words. Then we find that PH and CS methods degrade as the number of constraints increases, but they have complementary strengths. Specifically, PH is better at retaining high constraint accuracy but lower translation quality as measured by BLEU and COMET scores. In contrast, CS has the opposite results. Based on these observations, we propose a simple but effective method combining the advantages of PH and CS. This approach involves training a model like PH to predict the term labels, and then during inference replacing those labels with target terminology text like CS, so that the subsequent generation is aware of the target term content. Extensive experimental results show that this approach can achieve high constraint accuracy and translation quality simultaneously, regardless of the number or length of constraints.1

heading: 1 Introduction
text: Although Neural Machine Translation (NMT) has achieved expressive performance improvement with the increase of model and data scale, it still struggles when involved in mismatched domains and rare entities (Koehn and Knowles, 2017). Terminology constraints (TC) is a popular solution that requires the model to generate the translation following the pre-provided terminology pairs and
∗Corresponding author. 1https://github.com/zhajiahe/RTT
has been widely applied in commercial translation systems, such as Google, DeepL, etc.
Perhaps the most popular approach for TC is learning the constraint-aware model through data augmentation (Song et al., 2019; Dinu et al., 2019; Ailem et al., 2021; Bergmanis and Pinnis, 2021).2 Early data augmentation is based on placeholder (PH). During training, PH methods replace the terminology terms in both source and target sentences with ordered labels (e.g., “T1”, “T2”), while the model predicts labels rather than the concrete terms at inference (Crego et al., 2016; Michon et al., 2020). The main drawback of PH methods is that the term labels lose the original semantic information, resulting in incoherent translation. Unlike PH methods, Code-Switch (CS) methods follow the standard model and generate term translations word by word by injecting target constraints in the source sequence (Song et al., 2019; Dinu et al., 2019; Ailem et al., 2021).
In this work, we focus on understanding the robustness of existing terminology constraint methods in challenging constraint settings in practice. Our contributions are four-fold:
• We point out that the widely used terminology constraint test sets (IATE3, Wiktionary4, TICO5) are too oversimplified to evaluate the robustness. To address this, we have created a new, challenging English-German terminology constraint test set containing 500 sentence pairs with multiple long constraints. This proposed test set significantly increases the average number of constraints from 1.1∼1.7 to 6.1, and the target constraint length from
2We notice that all participating systems in the WMT21 Terminology Translation Task adopt this kind of method (Barrault et al., 2021).
3https://github.com/mtresearcher/terminology_ dataset/tree/master/iate
4https://github.com/mtresearcher/terminology_ dataset/tree/master/wiktionary
5https://tico-19.github.io/
6029
1.1∼1.2 words to 3.4 words. We will release this benchmark to promote the development of robust terminology translation.
• Through the proposed test set, we reveal that the performance of both Placeholder and Code-Switch degrades seriously with the increase of constraint count/length. However, it shows a strong complementarity in terms of constraint accuracy and translation quality; Placeholder is better at preserving accurate constraint, while Code-Switch yields higher translation quality as measured by COMET.
• Inspired by our findings, we propose a simple yet effective method for robust terminology translation (RTT), combining PH and CS’s advantages. RTT learns to predict the term label and achieves a high constraint accuracy (like PH). Once a term label is generated, RTT appends the constraint counterpart in the decoding sequence to make the consequence generation aware of the semantic constraints (like CS).
• The experimental results of IATE, Wiktionary, and the proposed test set demonstrate that our approach can attain higher constraint accuracy and translation quality compared to using PH or CS alone, regardless of the number and length of the constraints. In addition, RTT maintains a slightly faster inference speed than the vanilla Transformer.

heading: 2 Background
text: Let x = {x1, . . . , xM} be the source sentence, y = {y1, . . . , yN} be the target sentence, and C = {⟨s1, t1⟩, . . . , ⟨sK , tK⟩} be the constraint set about x and y, where si and ti are the i-th source and target constraint respectively. Each constraint could be multi-word, e.g., |si| >= 1, |ti| >= 1. Then TC asks the system must translate si into ti. In this section, we briefly introduce two typical TC methods based on data augmentation: Placeholder (PH) (Crego et al., 2016) and Code-Switch (CS) (Song et al., 2019; Dinu et al., 2019). We also describe some variants of them. Figure 1 explains the differences between these methods.
Placeholder. Placeholder is an early method for incorporating terminology constraints into machine translation. During training, the raw bitext is
pre-processed by replacing source and target constraints with corresponding ordered labels Ti. At inference time, source constraints are marked as ordered labels, and the model predicts the labels autonomously. The translation result is then obtained by replacing the labels with their corresponding target constraints in a post-processing step.
Code-Switch. Instead of using ordered labels, Code-Switch directly substitutes the source constraints with the corresponding target constraints in the input sentence. This allows the model to learn to copy the pre-specified target constraints from the input, so the decoder only needs to generate the target token step-by-step, like a standard system.
Variants. Considering the source side, vanilla PH and CS lose the source constraints’ semantics due to direct replacement by labels or target constraints. A simple yet efficient solution is to retain the source constraints but use a tag to distinguish them from the replacement marks, as proposed by Dinu et al. (2019). We refer to this variant as the source-enhanced model (SE). For PH, we can further additionally tag target constraint information in the input sentence, denoted as the targetenhanced model (TE). Since CS has already injected target constraints into the input sentence, TE is not available for it.

heading: 3 On the robustness of terminology constraint
text: In this section, we explore the robustness of existing TC solutions from two aspects: (1) number of constraints and (2) target constraint length. We first point out the oversimplified problem in existing TC test sets in Section 3.1. We describe our proposed
challenging TC test set in Section 3.2. Then we conduct comprehensive experiments to analyze the robustness of prior TC solutions in Section 3.3.

heading: 3.1 Oversimplified problem
text: As summarized in Table 2, oversimplified terminology constraint setups are widely present in published test sets, such as IATE, Wiktionary (Dinu et al., 2019), and TICO (Barrault et al., 2021), as well as extracted from alignment data (called EFA) (Wang et al., 2022; Guanhua et al., 2021). Typically, most open-source test sets have only one constraint per sentence, and the target constraint is also short, usually consisting of a single word. We suspect that this easy test set may lead to a misunderstanding of the practical performance of different methods. Intuitively, PH/PH+SE may suffer from poor translation fluency due to more target constraints, as the contents of these constraints are invisible during the generation of the decoder. However, this is not a severe problem for CS/CS+SE. On the other hand, PH should be insensitive to the constraint length, as it uses a single label as an alternative. In contrast, it is more difficult for CS to generate a long constraint due to more decoding steps required.

heading: 3.2 Proposed test set
text: To shed light on this issue, we made up a challenging TC test set. We notice that previous TC test sets generally are made by matching pre-build term database (e.g. IATE, Wiktionary) on existing bitext data sets. Since the term set is not strongly related to bitext, the number of matched constraints is not controlled. Instead, we first decide on the bitext data and then ask the linguistics expert to pick suitable sentence pairs to label constraints satisfying the requirement.
Specifically, we first collect WMT 13-18 test sets on English-German news translation task as the bitext data (14585 sentence pairs); The linguis-
tic expert artificially hand-picks 500 sentence pairs for the study. These pairs are designed to include a minimum of 6 constraints each, drawn from a carefully curated set of noun phrases (such as the names of organizations, persons, movies and brands) and common expressions. By focusing on these types of constraints, the expert aims to replicate the linguistic conditions found in industrial systems as closely as possible. Table 10 in Appendix shows some samples in the proposed test set.

heading: 3.3 Experiments
text: Setup. We conduct experiments on the WMT16 En-De task (4.5M). We replicate the same data processing as Vaswani et al. (2017) with 32k joined BPE codes. We use the standard transformer-base model setting: 6-layer encoder/decoder, 8 attention heads, hidden size of 512, and FFN hidden size of 2048. We train all models with 65536 batch tokens for 120k updates and use checkpoint average of the last 5 checkpoints. To apply constraints on training data, we extract terminologies from two publicly available term databases, Wiktionary and IATE. In order to avoid spurious matches, we filtered out the top 10k frequent words in term databases. According to previous work (Dinu et al., 2019), the augmented data size is about 10% of the original data. We compare five TC models from two families, including PH, PH+SE, PH+SE+TE, CS, and CS+SE. The difference lies in augmented data is shown in Figure 1.
Metrics. We use several metrics to study the performance of different methods comprehensively. Specifically, in addition to reporting detokenized BLEU scores with sacrebleu6 (Post, 2018), we also use COMET7 (Rei et al., 2020) to evaluate the translation quality, inspired by the inconsistent trend in recent study (Helcl et al., 2022). Besides, we use strict sentence-level constraint accuracy (SCA) as the metric for terminology constraint. That is to say, only translations that satisfy all constraints in the sentence are considered correct. In contrast, most previous studies consider term-level constraint accuracy (TCA). Compared to TCA, SCA is more desired in the practical system because the translation may be severely misunderstood even if only one constraint is wrong.
6BLEU+case.mixed+numrefs.1+smooth.exp+tok.13a +version.1.5.1
7wmt20-comet-da
Results on various constraint counts. To simulate the case of various constraint counts, suppose there are N constraints for each sentence pair in the proposed test set, we randomly pick up 1,. . . ,N constraints. As a result, we conduct k TC test sets with constraint count ranges from 1 to k, denoted by T1, . . . , Tk, where every pair in Ti has exactly i constraints. Table 3 shows the results of three metrics (BLEU, COMET, SCA) along with the number of constraint counts (k = 6). We can see that:
(i) The SE variants based on either PH or CS significantly improve translation quality in terms of BLEU and COMET, which indicates that it is necessary to make the model aware of source terminology semantics. The exceptions are the SCA results when increasing Ti. The possible reason is that injecting too much non-source information (e.g., label, target constraints) in the input confuses the model, decreasing the copying success rate.
(ii) The PH family performs better in SCA than the CS family, especially for larger Ti. For example, the gap between PH and CS is 8.8% in T1, extending to 26.8% in T6. To our best knowledge, it is the first time to reveal that dramatic SCA degradation in CS models.
(iii) According to COMET, the family of CS has a superior translation quality compared to the PH family. We contend that COMET is a crucial supplement to BLEU for assessing terminology constraints. We observe that PH+SE and CS+SE have similar average BLEU scores, yet there is a substantial performance gap in COMET. This is due to BLEU’s insensitivity to syntactic errors, whereas COMET imposes a hefty penalty, which is in line with earlier finding (Helcl et al., 2022).

heading: L Count None PH PH+SE PH+SE+TE CS CS+SE
text: Results on various target constraint lengths. To study the impact of target constraint lengths, we report the TCA on different constraint length in the proposed test set as shown in Table 4. Like the trend of SCA in various constraint counts, we find that the PH family is again significantly superior to the CS family, especially when the length becomes longer. This result also proves that the benefits of label prediction in terms of constraint accuracy exist widely in different situations.

heading: 4 Our approach

heading: 4.1 Basic idea
text: The above experiments empirically show the solid complementarity between PH and CS, and here we analyze the reason behind it (see Figure 1). We suppose there are two sequences impacting the decoding process: prediction sequence and context sequence, where the former is the realistic prediction by the model, and the latter decides the target context exposed to the model. For both PH-like and CS-like methods, the common problem is that they share the two sequences. Specifically, using placeholders in PH simplifies the prediction sequence but leads to the loss of constraint information (Figure 1a). In contrast, CS can observe the completed context but is redundant in the prediction sequence
(Figure 1b). Thus, we propose to decouple the two sequences, which is the basis of our approach, referred to as RTT. As illustrated in Figure 1c, we still use placeholders to simplify the prediction sequence, but expose their semantics to future tokens by replacing the placeholder with its text. We explain how to efficiently implement RTT in both training and inference in the following section.

heading: 4.2 Training
text: RTT is agnostic to model architecture, and here we use the vanilla Transformer due to its wide application. Figure 2 illustrates the overall architecture.
Data augmentation. Since RTT behaves the same as PH for the source side, we only describe the data augmentation on the target side. Consider the target sentence y = {y1, . . . , yN} and constraints C = {⟨s1, t1⟩, . . . , ⟨sK , tK⟩}, then we construct a new target sequence y∗ by prepending an ordered term label Ti before the beginning of constraint ti. For instance, in Figure 2, we aug-
ment the original target input “a, b, c, d, e, f, g” by “a, b, T1, c, d, e, T2, f, g”. We do not use any tags to distinguish term labels from normal tokens further to minimize the target sequence length.
Input embedding. In addition to the word embedding and sinusoidal positional embedding utilized in the standard Transformer, we introduce an additional learnable term embedding at the input layer. This term embedding provides information to the model about the number of constraints generated up to position i, thereby reducing the likelihood of generating repetitive constraints. Then the three embeddings are element-wise added to serve as the input to the Transformer layer. We note that the increase in the parameter size, K × d, due to the inclusion of the term embedding is negligible compared to the overall network parameters. Here, K represents the maximum number of constraints in a sentence, and d corresponds to the hidden size. In our work, we set K to be 64.
Control visible context. In the Code-Switch method, term labels T are not present during the translation generation. To replicate this behavior, we suggest using a mask matrix in the self-attention layer of the RTT’s decoder to make T invisible for subsequent tokens. Let MN×N be the mask matrix of the decoder self-attention layer, where Mij = 1 implies that the j-th target token is visible for the ith target token. In the standard Transformer, M is a lower triangular matrix, which means that Mij = 1 if i ≤ j. However, RTT additionally requires that yi ̸= T and yj ̸= T , thus preventing term labels from being exposed to regular tokens.
Loss masking. In the context of RTT, we aim to encourage the model to focus more on learning to predict the term label T rather than the corresponding constraint tokens t. This is because once T is predicted, the corresponding constraint tokens t will be automatically appended. To achieve this goal, we propose "Loss Masking" to guide the model’s attention. Specifically, for each token yi in the target sequence, we introduce a weight wi to modify the original log-likelihood log(P (yi)) by wi × log(P (yi)). Then, we assign wi = 1 to normal tokens or term labels in the target sequence. However, we set the weight wi to 0 for tokens that correspond to the target constraint. This is also equivalent to treating the target constraint tokens as padding symbols. It is important to note that even though the target constraint tokens are masked, they can still be learned from the raw training data.

heading: 4.3 Inference
text: RTT follows the autoregressive translation paradigm. At decoding step i, if the prediction ŷi is a normal token, it is appended to the decoding sequence and the next step is taken. However, if ŷi is a term label, the sequence will also contain its corresponding target constraint retrieved from the input term base. The use of beam search in RTT can complicate this process, as other translation candidates must add several PADs (padding symbols) to compensate for the increased sequence length when a term label is generated. This can lead to a larger footprint and higher computational costs at inference, especially when the number of constraints or beam size is larger. To address this issue, we propose a dynamic padding strategy that reduces the number of redundant PADs. As shown in Figure 3, we append PADs at the beginning of the sequence rather than the end of a term label. This allows us to truncate the longest portion of common PADs once all candidates have some PADs at the beginning of the sequence, resulting in a shorter sequence. The effectiveness of this implementation trick is shown in Figure 5.

heading: 5 Experimental results
text: We first validate the effectiveness of proposed approach on the same setup as Section 3.3. Then, for fair comparisons to existing work, we also conduct experiments with WMT18 En-De training data (Europarl, News Commentary) and common test sets (IATE, Wiktionary).

heading: 5.1 Results on proposed test set
text: We compared the performance of our proposed RTT model with two types of baseline methods: Placeholder approaches (PH) and Code-Switch approaches (CS). We also included the Transformer model as a baseline for comparison. Table 5 shows the average results of BLEU, COMET and SCA on our proposed test sets (T1, . . . , T6). Unlike the PH family or CS family, which are either proficient in BLEU/COMET or SCA, our proposed RTT model achieves high translation quality and constraint accuracy at the same time. Specifically, the proposed RTT model with source enhancement (RTT+SE) achieved the highest BLEU score, with an average score of 40.2. It also achieved the highest COMET score, with an average of 0.4866. In terms of SCA, although RTT+SE slightly falls behind the best system (PH), it outperforms CS+SE in a significant performance gap (about 20%). Similar to PH, we note that additionally applying TE to RTT+SE is not consistently optimal. Therefore, unless otherwise stated, we take RTT+SE as our primary model in the following experiments. We note that the use of source enhancement is critical. Otherwise, the pure RTT model degrades severely due to asymmetric constraint information between the source and target side. That is, the constraints on the source side are term labels, while those on the target side are constraint text. To make the improvement of RTT clear, we also draw performance curves along with the change in the number of constraints, as
illustrated in Figure 4.

heading: 5.2 Comparisons to existing methods
text: To compare RTT fairly with existing methods, we perform additional experiments on WMT18 En-De task and replicate Dinu et al. (2019)’s setup. We use Europarl and News Commentary data as training data (2.2M), and report BLEU (sacrebleu) and TERM accuracy (TCA) on two easy TC test sets (IATE, Wiktionary). We consider several systems as our baselines, such as Transformer (Vaswani et al., 2017), Const. Dec. (Post and Vilar, 2018), Source. Fact. (Dinu et al., 2019) and TADA (Ailem et al., 2021). The results of our experiments are shown in Table 6. Our proposed RTT model with source enhancement (RTT + SE) achieved the highest BLEU score on both test sets, with 27.2 on IATE and 27.8 on Wiktionary. It also achieved the highest TCA on the IATE test set, with a score of 99.6%. On the Wiktionary test set, the RTT model achieved a TCA score of 98.3%, which was slightly lower than the constraint decoding method but still significantly higher than the other methods. Overall, the results indicate that our proposed RTT model is not only capable of handling difficult constraints, but also works well on such easy test sets.

heading: 6 Analysis

heading: 6.1 Inference speed
text: As illustrated in Figure 5, we compared the decoding step size and inference speed between our model and the vanilla Transformer. We also study the effect when our model decodes with naive padding (NP) and dynamic padding (DP). It is clear
that the decoding step of NP is linearly increasing along with the number of constraints. Instead, the DP strategy successfully reduces an average of 52% decoding step and is very close to the baseline. As a bonus, the shorter decoding step in DP leads to a faster inference speed than NP. We note that RTT with DP can also run faster than the Transformer baseline when the constraint count is large because the corresponding target constraints in RTT are directly substituted to avoid costly model generation.

heading: 6.2 RTT without training
text: Without training, RTT can also be regarded as a modified Placeholder method. That is, the replacement of term labels transformers from the end of generation (as post-process) to the generation period. We are interested in whether the performance of Placeholder methods can be improved by plugand-play the inference part of RTT. To this end, we tested it on two pre-trained models: PH and PH+SE, and Table 7 listed the results. We can see that the impact of RTT inference is different: PH+SE benefits in COMET (+0.0061) and SCA (+1.4%), while all metrics degrade in the vanilla PH model. We attribute it to asymmetry constraint information between the source and target like RTT and RTT+SE. Specifically, RTT inference makes the model aware of the semantics of constraint, while the source side of PH loses information. Even so, the improvement in PH+SE indicates that RTT inference can be used directly on the existing PH+SE model without further training.

heading: 6.3 Ablation study
text: In Table 8, we demonstrate the effects of two training components: term embedding (TermE) and loss masking (LM). As expected, using TermE and LM yields the best performance, as indicated by the
highest scores on all three evaluation metrics. Not utilizing either component leads to a decrease in performance. Notably, LM has a greater effect than TermE, suggesting that allowing the model to focus on learning the desired targets is essential. The model appears less sensitive to TermE, likely because the word embedding of the introduced term label implicitly informs the model of the state of constraints.

heading: 7 Related work
text: There have been several approaches to addressing the issue of translating specialized terminology in the field of machine translation. One branch of approaches focuses on the decoding process, such as extending the search space (Hokamp and Liu, 2017; Post and Vilar, 2018; Hu et al., 2019) or using a finite-state acceptor (Hasler et al., 2018), to enforce terminology translation strictly. However,
these methods can incur high calculation costs and often result in poor translation quality (Guanhua et al., 2021). Another branch of approaches aims to modify the network architecture to better integrate with external terminologies through the use of alignment information (Song et al., 2020; Guanhua et al., 2021), vectorized terminology representation (Wang et al., 2022), or non-autoregressive translation (Susanto et al., 2020). These methods can potentially improve the integration of terminologies, but the big changes in network architecture greatly reduce their usability.
Data augmentation perhaps be the most widely used approach for terminology translation in machine translation. The placeholder method is an early solution for terminology translation by introducing special term labels (Crego et al., 2016). Michon et al. (2020) add linguistic information in the label to compensate for the semantic loss. Although effective, Placeholder techniques have difficulties producing smooth translations. Recently, Code-Switch methods have become popular as it overcomes this problem by allowing the model to generate word-by-word constraint translation, like standard neural machine translation. Song et al. (2019) directly replaces the source constraint with its translation in the input sequence; Dinu et al. (2019) uses some tags to distinguish be-
tween source constraints and target constraints; Ailem et al. (2021) further improves performance by masking the source constraints; Bergmanis and Pinnis (2021) uses target lemma to make the model learn morphology knowledge. As observed in our experiments, Code-Switch methods are fluent in translation but degrade in constraint accuracy. In contrast, our approach attempts to combine the strengths of Placeholder and Code-Switch, achieving high translation quality and constraint accuracy simultaneously.

heading: 8 Conclusion
text: Our study has highlighted the importance of taking robustness into account when comparing different methods of terminology constraint translation. We have found that the Placeholder and Code-Switch families are superior in different metrics, and the gap between them increases when dealing with more and longer terms. Additionally, we have observed that current TC test sets are inadequate for testing the robustness of different methods. To address this problem, we have created a new, more difficult terminology constraint test set. Moreover, we have proposed the RTT model, which merges the best features of the Placeholder and Code-Switch approaches and is capable of delivering both high translation quality and constraint accuracy regardless of the number of constraints and their length.
Limitations
While our proposed method demonstrates high translation quality and constraint accuracy, it is important to acknowledge that the hard copy mechanism may not be suitable for certain morphologically complex languages, such as Arabic. In Arabic, phrases or terminologies often involve conjunctions or prepositions and exhibit varying morphological forms. Unfortunately, our proposed method is not capable of effectively handling such cases, and addressing this challenge remains an open area for future research.

heading: Acknowledgements
text: We would like to thank the anonymous reviewers for their helpful comments. We also thank Shuqin Pan for the writing suggestions.

heading: A Appendix
text: A.1 Samples of different terminology constraint test sets
We pick samples from IATE, Wiktionary, and our proposed test set randomly and show them in Table 10.
A.2 Detailed settings We take two different settings for proposed test set and previous public test set, the detailed settings are listed in Table 9.
A.3 Samples of translation results Table 11 shows the translation result of different systerms.
ACL 2023 Responsible NLP Checklist

heading: A For every submission:
text: 3 A1. Did you describe the limitations of your work?
Section Limitations
7 A2. Did you discuss any potential risks of your work? Our work attend to a new method for terminology constraint and hope to benefit terminology translation in the future.
3 A3. Do the abstract and introduction summarize the paper’s main claims? Section Abstract and Introduction
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 3 Did you use or create scientific artifacts? Section 3
3 B1. Did you cite the creators of artifacts you used? Section 3

heading: B2. Did you discuss the license or terms for use and / or distribution of any artifacts?
text: Not applicable. Left blank.

heading: B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided
text: that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Not applicable. Left blank.

heading: B4. Did you discuss the steps taken to check whether the data that was collected / used contains any
text: information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank.

heading: B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and
text: linguistic phenomena, demographic groups represented, etc.? Not applicable. Left blank.
B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Not applicable. Left blank.
C 3 Did you run computational experiments? Section 3,5,6
3 C1. Did you report the number of parameters in the models used, the total computational budget (e.g., GPU hours), and computing infrastructure used? Section Appendix
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.
3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found hyperparameter values? Section 3
3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 3,5,6
3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 3
D 3 Did you use human annotators (e.g., crowdworkers) or research with human participants? section 3.2

heading: D1. Did you report the full text of instructions given to participants, including e.g., screenshots,
text: disclaimers of any risks to participants or annotators, etc.? Not applicable. Left blank.

heading: D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students)
text: and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? Not applicable. Left blank.

heading: D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to
text: crowdworkers explain how the data would be used? Not applicable. Left blank.

heading: D4. Was the data collection protocol approved (or determined exempt) by an ethics review board?
text: Not applicable. Left blank.

heading: D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data?
text: Not applicable. Left blank.

heading: Future Work
text: Did you discuss any potential risks of your work? Our work attend to a new method for terminology constraint and hope to benefit terminology translation in the future. 3 A3. Do the abstract and introduction summarize the paper’s main claims? Section Abstract and Introduction
7 A4. Have you used AI writing assistants when working on this paper? Left blank. B 3 Did you use or create scientific artifacts? Section 3
3 B1. Did you cite the creators of artifacts you used? Section 3