United Arab Emirates

This paper presents IIGroup&#39;s submission to the WMT22 Word-Level AutoCompletion(WLAC) Shared Task in four language directions. We propose to use a Generate-then-Rerank framework to solve this task. More specifically, the generator is used to generate candidate words and recall as many positive candidates as possible. To facilitate the training process of the generator, we propose a span-level mask prediction task. Once we get the candidate words, we take the top-K candidates and feed them into the reranker. The reranker is used to select the most confident candidate. The experimental results in four language directions demonstrate the effectiveness of our systems. Our systems achieve competitive performance ranking 1st in English to Chinese subtask and 2nd in Chinese to English subtask.

EMNLP 2022

IIGROUP Submissions for WMT22 Word-Level AutoCompletion Task

autocompletion

interactive machine translation

machine translation

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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

This paper presents IIGroup's submission to the WMT22 Word-Level AutoCompletion(WLAC) Shared Task in four language directions. We propose to use a Generate-then-Rerank framework to solve this task. More specifically, the generator is used to generate candidate words and recall as many positive candidates as possible. To facilitate the training process of the generator, we propose a span-level mask prediction task. Once we get the candidate words, we take the top-K candidates and feed them into the reranker. The reranker is used to select the most confident candidate. The experimental results in four language directions demonstrate the effectiveness of our systems. Our systems achieve competitive performance ranking 1st in English to Chinese subtask and 2nd in Chinese to English subtask.

workshop paper

This paper presents the submissions of Huawei Translation Services Center (HW-TSC) to WMT 2022 Word-Level AutoCompletion Task. We propose an end-to-end autoregressive model with bi-context based on Transformer to solve current task. The model uses a mixture of subword and character encoding units to realize the joint encoding of human input, the context of the target side and the decoded sequence, which ensures full utilization of information. We uses one model to solve four types of data structures in the task. During training, we try using a machine translation model as the pre-trained model and fine-tune it for the task. We also add BERT-style MLM data at the fine-tuning stage to improve model performance. We participate in zh\$\textbackslash{}rightarrow\$en, en\$\textbackslash{}rightarrow\$de, and de\$\textbackslash{}rightarrow\$en directions and win the first place in all the three tracks. Particularly, we outperform the second place by more than 5\textbackslash{}\% in terms of accuracy on the zh\$\textbackslash{}rightarrow\$en and en\$\textbackslash{}rightarrow\$de tracks. The result is buttressed by human evaluations as well, demonstrating the effectiveness of our model.

HW-TSC's Submissions to the WMT22 Word-Level Auto Completion Task

This paper describes the joint submission of Alibaba and Soochow University to the WMT 2022 Shared Task on Translation Suggestion (TS). We participate in the English to/from German and English to/from Chinese tasks. Basically, we utilize the model paradigm fine-tuning on the downstream tasks based on large-scale pre-trained models, which has recently achieved great success. We choose FAIR's WMT19 English to/from German news translation system and MBART50 for English to/from Chinese as our pre-trained models. Considering the task's condition of limited use of training data, we follow the data augmentation strategies provided by Yang to boost our TS model performance. And we further involve the dual conditional cross-entropy model and GPT-2 language model to filter augmented data. The leader board finally shows that our submissions are ranked first in three of four language directions in the Naive TS task of the WMT22 Translation Suggestion task.

TSMind: Alibaba and Soochow University's Submission to the WMT22 Translation Suggestion Task

This paper describes the Transn's submissions to the WMT2022 shared task on Translation
Suggestion. Our team participated on two tasks: Naive Translation Suggestion and Translation
Suggestion with Hints, focusing on two language directions Zh→En and En→Zh. Apart from the golden training data provided by the shared task, we utilized synthetic corpus to fine-tune on DeltaLM (∆LM), which is a pre-trained encoder-decoder language model. We applied two-stage training strategy on ∆LM and several effective methods to generate synthetic corpus, which contribute a lot to the results. According to the official evaluation results in terms of BLEU scores, our submissions in Naive Translation Suggestion En→Zh and Translation Suggestion with Hints (both Zh→En and En→Zh) ranked 1st, and Naive Translation Suggestion Zh→En also achieved comparable result to the best score.

Transn's Submissions to the WMT22 Translation Suggestion Task

Translation suggestion (TS) models are used to automatically provide alternative suggestions for incorrect spans in sentences generated by machine translation. This paper introduces the system used in our submission to the WMT'22 Translation Suggestion shared task. Our system is based on the ensemble of different translation architectures, including Transformer, SA-Transformer, and DynamicConv. We use three strategies to construct synthetic data from parallel corpora to compensate for the lack of supervised data. In addition, we introduce a multi-phase pre-training strategy, adding an additional pre-training phase with in-domain data. We rank second and third on the English-German and English-Chinese bidirectional tasks, respectively.

Improved Data Augmentation for Translation Suggestion

The success of large BERT models has raised the demand for model compression methods to reduce model size and computational cost. Quantization can reduce the model size and inference latency, making inference more efficient, without changing its stucture, but it comes at the cost of performance degradation. Due to the complex loss landscape of ternarized/binarized BERT, we present an efficient two-stage progressive quantization method in which we fine tune the model with quantized weights and progressively lower its bits, and then we fine tune the model with quantized weights and activations. At the same time, we strategically choose which bitwidth to fine-tune on and to initialize from, and which bitwidth to fine-tune under augmented data to outperform the existing BERT binarization methods without adding an extra module, compressing the binary model 18% more than previous binarization methods or compressing BERT by 31x w.r.t. to the full-precision model. Our method without data augmentation can outperform existing BERT ternarization methods.

Efficient Two-Stage Progressive Quantization of BERT

Link Prediction is the task of predicting missing relations between knowledge graph entities (KG). Recent work in link prediction mainly attempted to adapt a model to increase link prediction accuracy by using more layers in neural network architecture, which heavily rely on computational resources. This paper proposes the refinement of knowledge graphs to perform link prediction operations more accurately using relatively fast translational models. Translational link prediction models have significantly less complexity than deep learning approaches; this motivated us to improve their accuracy. Our method uses the ontologies of knowledge graphs to add information as auxiliary nodes to the graph. Then, these auxiliary nodes are connected to ordinary nodes of the KG that contain auxiliary information in their hierarchy. Our experiments show that our method can significantly increase the performance of translational link prediction methods in Hit@10, Mean Rank, and Mean Reciprocal Rank.

KGRefiner: Knowledge Graph Refinement for Improving Accuracy of Translational Link Prediction Methods

Neural language models have seen a dramatic increase in size in the last years. While many still advocate that `bigger is better', work in model distillation has shown that the number of parameters used by very large networks is actually more than what is required for state-of-the-art performance. This prompts an obvious question: can we build smaller models from scratch, rather than going through the inefficient process of training at scale and subsequently reducing model size.  In this paper, we investigate the behaviour of a biologically inspired algorithm, based on the fruit fly's olfactory system. This algorithm has shown good performance in the past on the task of learning word embeddings. We now put it to the test on the task of semantic hashing. Specifically, we compare the fruit fly to a standard binary network on the task of generating locality-sensitive hashes for text documents, measuring both task performance and energy consumption. Our results indicate that the two algorithms have complementary strengths while showing similar electricity usage.

Algorithmic Diversity and Tiny Models: Comparing Binary Networks and the Fruit Fly Algorithm on Document Representation Tasks

With 84.75 million Filipinos online, the ability for models to process online text is crucial for developing Filipino NLP applications. To this end, spelling correction is a crucial preprocessing step for downstream processing. However, the lack of data prevents the use of language models for this task. In this paper, we propose an N-Gram + Damerau-Levenshtein distance model with automatic rule extraction. We train the model on 300 samples, and show that despite limited training data, it achieves good performance and outperforms other deep learning approaches in terms of accuracy and edit distance. Moreover, the model (1) requires little compute power, (2) trains in little time, thus allowing for retraining, and (3) is easily interpretable, allowing for direct troubleshooting, highlighting the success of traditional approaches over more complex deep learning models in settings where data is unavailable.

Look Ma, Only 400 Samples! Revisiting the Effectiveness of Automatic N-Gram Rule Generation for Spelling Normalization in Filipino

Mixture of Experts (MoE) models with conditional execution of sparsely activated layers has enabled training models with a much larger number of parameters. As a result, these models have achieved significantly better quality on various natural language processing tasks including machine translation. However, it remains challenging to deploy such models in real-life scenarios due to the large memory requirements and inefficient inference. In this work, we introduce a highly efficient inference framework with several optimization approaches to accelerate the computation of sparse models and cut down the memory consumption significantly. While we achieve up to 26x speed-up in terms of throughput, we also reduce the model size almost to one eighth of the original 32-bit float model by quantizing expert weights into 4-bit integers. As a result, we are able to deploy 136x larger models with 27\% less cost and significantly better quality with large scale MoE model deployment compared to the existing solutions.  This enables a paradigm shift in deploying large scale multilingual MoE transformers models instead of distilling into dozens of smaller models per language or task.

Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production

Document retrieval is a core component of many knowledge-intensive natural language processing task formulations such as fact verification. Sources of textual knowledge such as Wikipedia articles condition the generation of answers from the models. Recent advances in retrieval use sequence-to-sequence models to incrementally predict the title of the appropriate Wikipedia page given an input instance. However, this method requires supervision in the form of human annotation to label which Wikipedia pages contain appropriate context.
This paper introduces a distant-supervision method that does not require any annotation train auto-regressive retrievers that attain competitive R-Precision and Recall in a zero-shot setting.
Furthermore we show that with task-specific supervised fine-tuning, auto-regressive retrieval performance for two Wikipedia-based fact verification tasks can approach or even exceed full supervision using less than  $1/4$ of the annotated data. 
We release all code and models

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