The sequence to sequence BIBREF0, BIBREF1 approach to Neural Machine Translation (NMT) has shown to improve quality in various translation tasks BIBREF2, BIBREF3, BIBREF4. While translation quality is normally measured in terms of correct transfer of meaning and of fluency, there are several applications of NMT that would benefit from optimizing the output length, such as the translation of document elements that have to fit a given layout – e.g. entries of tables or bullet points of a presentation – or subtitles, which have to fit visual constraints and readability goals, as well as speech dubbing, for which the length of the translation should be as close as possible to the length of the original sentence.Current NMT models do not model explicitly sentence lengths of input and output, and the decoding methods do not allow to specify desired number of tokens to be generated. Instead, they implicitly rely on the observed length of the training examples BIBREF5, BIBREF6.Sequence-to-sequence models have been also applied to text summarization BIBREF7 to map the relevant information found in a long text into a limited-length summary. Such models have shown promising results by directly controlling the output length BIBREF8, BIBREF9, BIBREF10, BIBREF11. However, differently from MT, text summarization (besides being a monolingual task) is characterized by target sentences that are always much shorter than the corresponding source sentences. While in MT, the distribution of the relative lengths of source and target depends on the two languages and can significantly vary from one sentence pair to another due to stylistic decisions of the translator and linguistic constraints (e.g. idiomatic expressions).In this work, we propose two approaches to control the output length of a transformer NMT model. In the first approach, we augment the source side with a token representing a specific length-ratio class, i.e. short, normal, and long, which at training time corresponds to the observed ratio and at inference time to the desired ratio. In the second approach, inspired by recent work in text summarization BIBREF11, we enrich the position encoding used by the transformer model with information representing the position of words with respect to the end of the target string.We investigate both methods, either in isolation or combined, on two translation directions (En-It and En-De) for which the length of the target is on average longer than the length of the source. Our ultimate goal is to generate translations whose length is not longer than that of the source string (see example in Table FIGREF1). While generating translations that are just a few words shorter might appear as a simple task, it actually implies good control of the target language. As the reported examples show, the network has to implicitly apply strategies such as choosing shorter rephrasing, avoiding redundant adverbs and adjectives, using different verb tenses, etc. We report MT performance results under two training data conditions, small and large, which show limited degradation in BLEU score and n-gram precision as we vary the target length ratio of our models. We also run a manual evaluation which shows for the En-It task a slight quality degradation in exchange of a statistically significant reduction in the average length ratio, from 1.05 to 1.01.Our proposal is based on the transformer architecture and a recently proposed extension of its positional encoding aimed to control the length of generated sentences in text summarization.Transformer BIBREF12 is a sequence-to-sequence architecture that processes sequences using only attention and feed forward layers. Its core component is the so-called multi-head attention, which computes attention BIBREF0, BIBREF13 between two sequences in a multi-branch fashion BIBREF14. Within the encoder or the decoder, each layer first computes attention between two copies of the same sequence (self-attention). In the decoder, this step is followed by an attention over the encoder output sequence. The last step in each layer is a two-layered time-distributed feed-forward network, with a hidden size larger than its input and output. Attention and feed-forward layers are characterized by a position-invariant processing of their input. Thus, in order to enrich input embeddings in source and target with positional information, they are summed with positional vectors of the same dimension $d$, which are computed with the following trigonometric encoding ($\text{PE}$):for $i=1,\ldots ,d/2$.Recently, an extension of the positional encoding BIBREF11 was proposed to model the output length for text summarization. The goal is achieved by computing the distance from every position to the end of the sentence. The new length encoding is present only in the decoder network as an additional vector summed to the input embedding. The authors proposed two different variants. The first variant replaces the variable pos in equations (1-2) with the difference $len - pos$, where len is the sentence length. The second variant attempts to model the proportion of the sentence that has been covered at a given position by replacing the constant 10000 in the denominator of equations (1-2) with $len$. As decoding is performed at the character level, len and pos are given in number of characters. At training time, len is the observed length of the reference summary, while at inference time it is the desired length.We propose two methods to control the output length in NMT. In the first method we partition the training set in three groups according to the observed length ratio of the reference over the source text. The idea is to let the model learn translation variants by observing them jointly with an extra input token. The second method extends the Transformer positional encoding to give information about the remaining sentence length. With this second method the network can leverage fine-grained information about the sentence length.Our first approach to control the length is inspired by target forcing in multilingual NMT BIBREF15, BIBREF16. We first split the training sentence pairs into three groups according to the target/source length ratio (in terms of characters). Ideally, we want a group where the target is shorter than the source (short), one where they are equally-sized (normal) and a last group where the target is longer than the source (long). In practice, we select two thresholds $t_\text{min}$ and $t_\text{max}$ according to the length ratio distribution. All the sentence pairs with length ratio between $t_\text{min}$ and $t_\text{max}$ are in the normal group, the ones with ratio below $t_\text{min}$ in short and the remaining in long. At training time we prepend a length token to each source sentence according to its group ($<$short$>$, $<$normal$>$, or $<$long$>$), in order to let a single network to discriminate between the groups (see Figure FIGREF2). At inference time, the length token is used to bias the network to generate a translation that belongs to the desired length group.Inspired by BIBREF11, we use length encoding to provide the network with information about the remaining sentence length during decoding. We propose two types of length encoding: absolute and relative. Let pos and len be, respectively, a token position and the end of the sequence, both expressed in terms of number characters. Then, the absolute approach encodes the remaining length:where $i=1,\ldots ,d/2$.Similarly, the relative difference encodes the relative position to the end. This representation is made consistent with the absolute encoding by quantizing the space of the relative positions into a finite set of $N$ integers:where $q_N: [0, 1] \rightarrow \lbrace 0, 1, .., N\rbrace $ is simply defined as $q_N(x) = \lfloor {x \times N}\rfloor $. As we are interested in the character length of the target sequence, len and pos are given in terms of characters, but we represent the sequence as a sequence of BPE-segmented subwords BIBREF17. To solve the ambiguity, len is the character length of the sequence, while pos is the character count of all the preceding tokens. We prefer a representation based on BPE, unlike BIBREF11, as it leads to better translations with less training time BIBREF18, BIBREF19. During training, len is the observed length of the target sentence, while at inference time it is the length of the source sentence, as it is the length that we aim to match. The process is exemplified in Figure FIGREF9.We further propose to use the two methods together to combine their strengths. In fact, while the length token acts as a soft constraint to bias NMT to produce short or long translation with respect to the source, actually no length information is given to the network. On the other side, length encoding leverages information about the target length, but it is agnostic of the source length.Training an NMT model from scratch is a compute intensive and time consuming task. Alternatively, fine-tuning a pre-trained network shows to improve performance in several NMT scenarios BIBREF20, BIBREF21, BIBREF22, BIBREF23, BIBREF24. For our length control approaches, we further propose to use fine-tuning an NMT model with length information, instead of training it from scratch. By adopting a fine-tuning strategy, we specifically aim; i) to decouple the performance of the baseline NMT model from that of the additional length information, ii) control the level of aggressiveness that can come from the data (length token) and the model (length encoding), and iii) make the approaches versatile to any pre-trained model. More importantly, it will allow to transform any NMT model to an output length aware version, while getting better improvements on the quality of the generated sequences.Our experiments are run using the English$\rightarrow $Italian/German portions of the MuST-C corpus BIBREF25, which is extracted from TED talks, using the same train/validation/test split as provided with the corpus (see Table TABREF18). As additional data, we use a mix of public and proprietary data for about 16 million sentence pairs for English-Italian (En-It) and $4.4$ million WMT14 sentence pairs for the English-German (En-De). While our main goal is to verify our hypotheses on a large data condition, thus the need to include proprietary data, for the sake of reproducibility in both languages we also provide results with systems only trained on TED Talks (small data condition). When training on large scale data we use Transformer with layer size of 1024, hidden size of 4096 on feed forward layers, 16 heads in the multi-head attention, and 6 layers in both encoder and decoder. When training only on TED talks, we set layer size of 512, hidden size of 2048 for the feed forward layers, multi-head attention with 8 heads and again 6 layers in both encoder and decoder.In all the experiments, we use the Adam BIBREF26 optimizer with an initial learning rate of $1\times 10^{-7}$ that increases linearly up to $0.001$ for 4000 warm-up steps, and decreases afterwards with the inverse square root of the training step. The dropout is set to $0.3$ in all layers but the attention, where it is $0.1$. The models are trained with label smoothed cross-entropy with a smoothing factor of $0.1$. Training is performed on 8 Nvidia V100 GPUs, with batches of 4500 tokens per GPU. Gradients are accumulated for 16 batches in each GPU BIBREF27. We select the models for evaluation by applying early stopping based on the validation loss. All texts are tokenized with scripts from the Moses toolkit BIBREF28, and then words are segmented with BPE BIBREF17 with 32K joint merge rules.For evaluation we take the best performing checkpoint on the dev set according to the loss. The size of the data clusters used for the length token method and their corresponding target-source length ratios are reported in Table TABREF19. The value of $N$ of the relative encoding is set to a small value (5), as in preliminary experiments we observed that a high value (100) produces results similar to the absolute encoding.We evaluate our Baseline Transformer using two decoding strategies: i) a standard beam search inference (standard), and ii) beam search with length penalty (penalty) set to $0.5$ to favor shorter translations BIBREF29.Length token models are evaluated with three strategies that correspond to the tokens prepended to the source test set at a time (short, normal, and long), and reported as Len-Tok. Length encoding (Len-Enc) models are evaluated in a length matching condition, i.e. output length has to match input length. We report the relative (Rel) and absolute (Abs) strategies of the approach as discussed in Section SECREF10. In the small data condition, we additionally evaluated how the fine-tuning strategy compares with a model trained from scratch. In the large data condition, we added a setting that combines both the length-token and length-encoding strategies.To evaluate all models' performance we compute BLEU BIBREF30 with the multi-bleu.perl implementation on the single-reference test sets of the En-It and En-De pairs. Given the absence of multiple references covering different length ratios, we also report n-gram precision scores (BLEU$^*$), by multiplying the BLEU score by the inverse of the brevity penalty BIBREF30. BLEU$^*$ scores is meant to measure to what extent shorter translations are subset of longer translations.The impact on translation lengths is evaluated with the mean sentence-level length ratios between MT output and source (LR$^{src}$) and between MT output and reference (LR$^{ref}$).We performed experiments in two conditions: small data and larger data. In the small data condition we only use the MuST-C training set. In the large data condition, a baseline model is first trained on large data, then it is fine-tuned on the MuST-C training set using the proposed methods. Tables TABREF23 and TABREF26 lists the results for the small and large data conditions. For the two language directions they show BLEU and BLEU* scores, as well as the average length ratios.The baselines generate translations longer than the source sentence side, with a length ratio of 1.05 for Italian and 1.11 for German. Decoding with length penalty (penalty) slightly decreases the length ratios but they are still far from our goal of LR$^{src}$=1.00.Fine-tuning. A comparison of the models trained from scratch (central portion of Table TABREF23) with their counterparts fine-tuned from the baseline (last portion of Table TABREF23) shows that the models in the first group generally generate shorter translations, but of worse quality. Additionally, the results with fine-tuning are not much different from the baseline. Existing models can be enhanced to produce shorter sentences, and little variation is observed in their translation quality.Length tokens. Fine-tuning with Len-Tok (Fourth section in Table TABREF23) gives a coarse-grained control over the length, while keeping BLEU scores similar to the baseline or slightly better. Decoding with the token normal leads to translations slightly shorter than the baseline for En-It (LR$^{src}$=1.05 and LR$^{ref}$=1.02), while the token small strongly reduces the translation lengths up to almost the source length (LR$^{src}$=1.01). In the opposite side, the token long generates longer translations which are slightly worse than the others (32.00). A similar behavior is observed for En-De, where the LR$^{src}$ goes from 1.12 to 1.07 when changing normal with short, and to 1.15 with long. The results with the token long are not interesting for our task and are given only for the sake of completeness.Length Encoding. The last section of Table TABREF23 lists the results of using length encoding (Len-Enc) relative (Rel) and absolute (Abs). The two encodings lead to different generated lengths, with Abs being always shorter than Rel. Unfortunately, these improvements in the lengths correspond to a significant degradation in translation quality, mostly due to truncated sentences.Our Baselines for the large data condition generate sentences with length ratios over the source comparable to the small data condition (LR$^\text{src}$ and LR$^\text{ref}$), but with better translation quality: 35.46 BLEU points for En-It and 33.96 for En-De. Length penalty slightly reduces the length ratios, which results in a 0.3 BLEU points improvement in Italian and -0.3 in German because of the brevity penalty. In the latter case, the BLEU* is slightly better than the standard baseline output. Also for the large data condition, while the length penalty slightly helps to shorten the translations, its effect is minimal and insufficient for our goal.Length tokens. In En-It there is no noticeable difference in translation quality between the tokens normal and short, while there is a degradation of $\sim 0.7$ points when using long. This last result is consistent with the ones observed before. Also in this case the token short does not degrade the BLEU score, and obtains the highest precision BLEU* with 36.22. In En-De we obtain the best results with token normal (34.46), which matches the length distribution of the references. The token short generates much shorter outputs (LR$^\text{src}$=1.05), which are also much shorter than the reference (LR$^\text{ref}=0.93$). Consequently the BLEU score degrades significantly (30.61), and also the BLEU* is 1 point lower than with the token normal. Longer translations can be generated with the token long, but they always come at the expense of lower quality.Length encoding. For En-It, Len-Enc Rel in Table TABREF26 achieves a LR$^\text{src}$ of 1.01 with a slight degradation of $0.3$ BLEU points over the baseline, while in the case of Abs the degradation is higher (-1.6) and LR$^\text{src}$ is similar (1.02). Also in En-De the degradation of Rel over the baseline is only -0.3, but the reduction in terms of LR$^\text{src}$ is very small (1.11 vs 1.13). On the other side, Abs produces much shorter translations (1.03 LR$^\text{src}$) at the expense of a significantly lower BLEU score (30.79). When computing the BLEU* score, the absolute encoding is only 0.45 points lower than the relative encoding (33.29 vs 33.74), but -0.8 lower than the baseline.Token + Encoding. So far, we have observed generally good results using the token method and translating with the tokens short and normal. while the length encoding generally produces a more predictable output length, in particular for the absolute variant. In the last experiment, we combine the two methods in order to have a system that can capture different styles (short, normal, long), as well as explicitly leveraging length information. The results listed in the last portion of Table TABREF26 (Tok+Enc) show that the relative encoding Rel produces better translations than Abs, but again it has less predictability in output length. For instance, in En-It the LR$^\text{src}$ of Rel is 0.96 with token short and 1.02 with normal, while for En-De it is 1.01 with short and 1.08 with normal. On the other side, the Abs produces LR$^\text{src}$ of 1.01 with both tokens in En-It and also with short in En-De, and it increases to only 1.03 with normal.Controlling output length. In order to achieve LR$^\text{src}$ as close as possible to 1.0, we set the target length during generation equal to the source length when using the length encoding methods. However, one advantage of length encoding is the possibility to set the target length to modify the average output length. We illustrate this option by using the Tok+Enc Rel system for En-It, and translating with the tokens normal or short and different scaling factors for the target length. The results, listed in Table TABREF27, show that we are able to approach an LR$^{src}$ of 1.0 with both tokens and the BLEU score is not affected with token normal (35.45) or improves with token short (35.11).Discussion. Length token is an effective approach to generate translations of different lengths, but it does not allow a fine-grained control of the output lengths and its results depend on the partition of the training set into groups, which is a manual process. Length encoding allows to change the output length, but the two variants have different effects. Absolute encoding is more accurate but generates sentences with missing information. The relative encoding produces better translations than the absolute encoding, but its control over the translation length is more loose. The increased length stability is captured by the standard deviation of the length ratio with the source, which is $0.14$ for length tokens, $\sim 0.11$ for relative encoding and $\sim 0.07$ for absolute encoding. The advantage of the combined approach is that it can generate sentences with different style to fit different length groups, and the output length can also be tuned by modifying the target length, while no important quality degradation is observed. Additionally, the standard deviation of the lengths is the same as for the length encoding used.After manually inspecting the outputs of the best performing models under the large data condition, we decided to run a human evaluation only for the En-It Len-Tok model. As our ultimate goal is to be able to generate shorter translations and as close as possible to the length of the source sentences, we focused the manual evaluation on the Short output class and aimed to verify possible losses in quality with respect to the baseline system. We ran a head-to-head evaluation on the first 10 sentences of each test talk, for a total of 270 sentences, by asking annotators to blindly rank the two system outputs (ties were also permitted) in terms of quality with respect to a reference translation. We collected three judgments for each output, from 19 annotators, for a total of 807 scores (one sentence had to be discarded). Inter-annotator agreement measured with Fleiss' kappa was 0.35 (= fair agreement). Results reported in Table TABREF32 confirm the small differences observed in BLEU scores: there are only a 4% more wins for the Baseline and almost 60% of ties. The small degradation in quality of the shorter translations is statistically significant ($p<0.05$), as well as their difference in length ($p<0.001$).Notice that the evaluation was quite severe towards the shorter translations, as even small changes of the meaning could affect the ranking. After the manual evaluation, we analyzed sentences in which shorter translations were unanimously judged equal or better than the standard translations. We hence tried to identify the linguistic skills involved in the generation of shorter translations, namely: (i) use of abbreviations, (ii) preference of simple verb tenses over compound tenses, (iii) avoidance of not relevant adjective, adverbs, pronouns and articles, (iv) use of paraphrases. Table TABREF33 shows examples of the application of the above strategies as found in the test set.As an integration of Section 2, we try to provide a more complete picture on previous work with seq-to-seq models to control the output length for text summarization, and on the use of tokens to bias in different ways the output of NMT.In text summarization, BIBREF8 proposed methods to control output length either by modifying the search process or the seq-to-seq model itself, showing that the latter being more promising. BIBREF9 addressed the problem similarly to our token approach, by training the model on data bins of homogeneous output length and conditioning the output on a length token. They reported better performance than BIBREF8. Finally, BIBREF11 proposed the extension of the positional encoding of the transformer (cf. Section 2), reporting better performance than BIBREF8 and BIBREF9.The use of tokens to condition the output of NMT started with the multilingual models BIBREF15, BIBREF16, and was then further applied to control the use of the politeness form in English-German NMT BIBREF32, in the translation from English into different varieties of the same language BIBREF33, for personalizing NMT to user gender and vocabulary BIBREF34, and finally to perform NMT across different translation styles BIBREF35.In this paper, we have proposed two solutions for the problem of controlling the output length of NMT. A first approach, inspired by multilingual NMT, allows a coarse-grained control over the length and no degradation in translation quality. A second approach, inspired by positional encoding, enables a fine-grained control with only a small error in the token count, but at the cost of a lower translation quality. A manual evaluation confirms the translation quality observed with BLEU score. In future work, we plan to design more flexible and context-aware evaluations which allow us to account for short translations that are not equivalent to the original but at the same time do not affect the overall meaning of the discourse.

Q: Do they conduct any human evaluation?

Q: What dataset do they use for experiments?

Q: How do they enrich the positional embedding with length information
A: They introduce new trigonometric encoding which besides information about position uses additional length information (abs or relative).

Q: How do they condition the output to a given target-source class?
A: They use three groups short/normal/long translation classes to learn length token, which is in inference used to bias network to generate desired length group.

Q: Which languages do they focus on?

Q: What dataset do they use?

Q: Do they experiment with combining both methods?