Neural machine translation systems estimate probabilities of target sentences given source sentences, yet these estimates may not align with human preferences. This work introduces QE-fusion, a method that synthesizes translations using a quality estimation metric (QE), which correlates better with human judgments. QE-fusion leverages a pool of candidates sampled from a model, combining spans from different candidates using a QE metric such as CometKiwi. We compare QE-fusion against beam search and recent reranking techniques, such as Minimum Bayes Risk decoding or QE-reranking. Our method consistently improves translation quality in terms of COMET and BLEURT scores when applied to large language models (LLMs) used for translation (PolyLM, XGLM, Llama2, Mistral, ALMA, and Tower) and to multilingual translation models (NLLB), over five language pairs. Notably, QE-fusion exhibits larger improvements for LLMs due to their ability to generate diverse outputs. We demonstrate that our approach generates novel translations in over half of the cases and consistently outperforms other methods across varying numbers of candidates (5–200). Furthermore, we empirically establish that QE-fusion scales linearly with the number of candidates in the pool.

Don’t Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation

Despite the impressive performance of large language models (LLMs), they
often lag behind specialized models in various tasks. LLMs only use a fraction
of the existing training data for in-context learning, while task-specific
models harness the full dataset for fine-tuning. In this work, we tackle the
problem of leveraging training data to improve the performance of LLMs without
fine-tuning. Our approach directly targets LLM predictions without requiring
access to their weights. We create a pool of candidates from the LLM through
few-shot prompting and we employ a compact model, the LM-corrector (LMCor),
specifically trained to merge these candidates to produce an enhanced output.
Our experiments on four natural language generation tasks demonstrate that even
a small LMCor model (250M) substantially improves the few-shot performance of
LLMs (62B), matching and even outperforming standard fine-tuning. Furthermore,
we illustrate the robustness of LMCor against different prompts, thereby
minimizing the need for extensive prompt engineering. Finally, we show that
LMCor can be seamlessly integrated with different LLMs at inference, serving as
a plug-and-play module to improve their performance.

Small Language Models Improve Giants by Rewriting Their Outputs

State-of-the-art multilingual systems rely on shared vocabularies that sufficiently cover all
considered languages. To this end, a simple and frequently used approach makes use of
subword vocabularies constructed jointly over several languages. We hypothesize that such
vocabularies are suboptimal due to false positives (identical subwords with different mean-
ings across languages) and false negatives (different subwords with similar meanings). To
address these issues, we propose Subword Mapping and Anchoring across Languages
(SMALA), a method to construct bilingual subword vocabularies. SMALA extracts sub-
word alignments using an unsupervised state-of-the-art mapping technique and uses them to create cross-lingual anchors based on subword similarities. We demonstrate the benefits of SMALA for cross-lingual natural language inference (XNLI), where it improves zero-shot
transfer to an unseen language without task-specific data, but only by sharing subword em-
beddings. Moreover, in neural machine translation, we show that joint subword vocabu-
laries obtained with SMALA lead to higher BLEU scores on sentences that contain many
false positives and false negatives.

Subword Mapping and Anchoring across Languages

Common acquisition functions for active learning use either uncertainty or diversity sampling, aiming to select difficult and diverse data points from the pool of unlabeled data, respectively. In this work, leveraging the best of both worlds, we propose an acquisition function that opts for selecting contrastive examples, i.e. data points that are similar in the model feature space and yet the model outputs maximally different predictive likelihoods. We compare our approach, CAL (Contrastive Active Learning), with a diverse set of acquisition functions in four natural language understanding tasks and seven datasets. Our experiments show that CAL performs consistently better or equal than the best performing baseline across all tasks, on both in-domain and out-of-domain data. We also conduct an extensive ablation study of our method and we further analyze all actively acquired datasets showing that CAL achieves a better trade-off between uncertainty and diversity compared to other strategies.

Active Learning by Acquiring Contrastive Examples

State-of-the-art multilingual systems rely on shared vocabularies that sufficiently cover all considered languages. To this end, a simple and frequently used approach makes use of subword vocabularies constructed jointly over several languages. We hypothesize that such vocabularies are suboptimal due to false positives (identical subwords with different meanings across languages) and false negatives (different subwords with similar meanings). To address these issues, we propose Subword Mapping and Anchoring across Languages (SMALA), a method to construct bilingual subword vocabularies. SMALA extracts subword alignments using an unsupervised state-of-the-art mapping technique and uses them to create cross-lingual anchors based on subword similarities. We demonstrate the benefits of SMALA for cross-lingual natural language inference (XNLI), where it improves zero-shot transfer to an unseen language without task-specific data, but only by sharing subword embeddings. Moreover, in neural machine translation, we show that joint subword vocabularies obtained with SMALA lead to higher BLEU scores on sentences that contain many false positives and false negatives.

This poster session includes Main Conference posters, TACL and Demos.

In-Person Poster Session 4

poster

### Welcome!
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Generation

technical paper

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As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.



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W11: Sixth Conference on Machine Translation Day 1

workshop paper

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

**Session Chair: **Iz Beltagy 
**In Person Volunteer:** Abhinav Kumar 
** Remote Volunteer:** Enfa Rose George

Georgios Vernikos

6

13

SHORT BIO

Presentations

Don’t Rank, Combine! Combining Machine Translation Hypotheses Using Quality Estimation

Small Language Models Improve Giants by Rewriting Their Outputs

Subword Mapping and Anchoring across Languages

Active Learning by Acquiring Contrastive Examples

Subword Mapping and Anchoring across Languages

Active Learning by Acquiring Contrastive Examples

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