United Arab Emirates

Multilingual neural machine translation aims to translate multiple language pairs in a single model and has shown great success thanks to the knowledge transfer across languages with the shared parameters. Despite promising, this share-all paradigm suffers from insufficient ability to capture language-specific features. Currently, the common practice is to insert or search language-specific networks to balance the shared and specific features. However, those two types of features are not sufficient enough to model the complex commonality and divergence across languages, such as the locally shared features among similar languages, which leads to sub-optimal transfer, especially in massively multilingual translation. In this paper, we propose a novel token-level feature mixing method that enables the model to capture different features and dynamically determine the feature sharing across languages. Based on the observation that the tokens in the multilingual model are usually shared by different languages, we we insert a feature mixing layer into each Transformer sublayer and model each token representation as a mix of different features, with a proportion indicating its feature preference. In this way, we can perform fine-grained feature sharing and achieve better multilingual transfer. Experimental results on multilingual datasets show that our method outperforms various strong baselines and can be extended to zero-shot translation. Further analyses reveal that our method can capture different linguistic features and bridge the representation gap across languages.

EMNLP 2022

Adaptive Token-level Cross-lingual Feature Mixing for Multilingual Neural Machine Translation

cross-lingual feature mixing

mixture-of-experts

multilingual neural machine translation

knowledge transfer

**Welcome to ACL-IJCNLP 2021!**

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

poster

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022! I believe this conference has been complicated beyond any precedent. Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm. We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community. Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

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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.

How to achieve neural machine translation with limited parallel data? Existing techniques often rely on large-scale monolingual corpus, which is impractical for some low-resource languages. In this paper, we turn to connect several low-resource languages to a particular high-resource one by additional visual modality. Specifically, we propose a cross-modal contrastive learning method to learn a shared space for all languages, where both a coarse-grained sentence-level objective and a fine-grained token-level one are introduced. Experimental results and further analysis show that our method can effectively learn the cross-modal and cross-lingual alignment with a small amount of image-text pairs, and achieves significant improvements over the text-only baseline under both zero-shot and few-shot scenarios.

Low-resource Neural Machine Translation with Cross-modal Alignment

Recent work on applying large language models (LMs) achieves impressive performance in many NLP applications. Adapting or posttraining an LM using an unlabeled domain corpus can produce even better performance for end-tasks in the domain. This paper proposes the problem of continually extending an LM by incrementally post-train the LM with a sequence of unlabeled domain corpora to expand its knowledge without forgetting its previous skills. The goal is to improve the few-shot end-task learning in these domains. The resulting system is called CPT (Continual PostTraining), which to our knowledge, is the first continual post-training system. Experimental results verify its effectiveness.

Continual Training of Language Models for Few-Shot Learning

We present DiscoSense, a benchmark for commonsense reasoning via understanding a wide variety of discourse connectives. We generate compelling distractors in DiscoSense using Conditional Adversarial Filtering, an extension of Adversarial Filtering that employs conditional generation. We show that state-of-the-art pre-trained language models struggle to perform well on DiscoSense, which makes this dataset ideal for evaluating next-generation commonsense reasoning systems.

DiscoSense: Commonsense Reasoning with Discourse Connectives

Indian Sign Language, though used by a diverse community, still lacks well-annotated resources for developing systems that would enable sign language processing. In recent years researchers have actively worked for sign languages like American Sign Languages, however, Indian Sign language is still far from data-driven tasks like machine translation. To address this gap, in this paper, we introduce a new dataset CISLR (Corpus for Indian Sign Language Recognition) for word-level recognition in Indian Sign Language using videos. The corpus has a large vocabulary of around 4700 words covering different topics and domains. Further, we propose a baseline model for word recognition from sign language videos. To handle the low resource problem in the Indian Sign Language, the proposed model consists of a prototype-based one-shot learner that leverages resource rich American Sign Language to learn generalized features for improving predictions in Indian Sign Language. Our experiments show that gesture features learned in another sign language can help perform one-shot predictions in CISLR.

CISLR: Corpus for Indian Sign Language Recognition

Text error correction aims to correct the errors in text sequences such as those typed by humans or generated by speech recognition models.
Previous error correction methods usually take the source (incorrect) sentence as encoder input and generate the target (correct) sentence through the decoder. Since the error rate of the incorrect sentence is usually low (e.g., 10\%), the correction model can only learn to correct on limited error tokens but trivially copy on most tokens (correct tokens), which harms the effective training of error correction. In this paper, we argue that the correct tokens should be better utilized to facilitate effective training and then propose a simple yet effective masking strategy to achieve this goal.
Specifically, we randomly mask out a part of the correct tokens in the source sentence and let the model learn to not only correct the original error tokens but also predict the masked tokens based on their context information. Our method enjoys several advantages: 1) it alleviates trivial copy; 2) it leverages effective training signals from correct tokens; 3) it is a plug-and-play module and can be applied to different models and tasks. Experiments on spelling error correction and speech recognition error correction on Mandarin datasets and grammar error correction on English datasets with both autoregressive and non-autoregressive generation models show that our method improves the correction
accuracy consistently.

Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction

Multimodal knowledge graph completion (MKGC) aims to predict missing entities in MKGs. Previous works usually share relation representation across modalities. This results in mutual interference between modalities during training, since for a pair of entities, the relation from one modality probably contradicts that from another modality. Furthermore, making a unified prediction based on the shared relation representation treats the input in different modalities equally, while their importance to the MKGC task should be different. In this paper, we propose MoSE, a Modality Split representation learning and Ensemble inference framework for MKGC. Specifically, in the training phase, we learn modality-split relation embeddings for each modality instead of a single modality-shared one, which alleviates the modality interference. Based on these embeddings, in the inference phase, we first make modality-split predictions and then exploit various ensemble methods to combine the predictions with different weights, which models the modality importance dynamically. Experimental results on three KG datasets show that MoSE outperforms state-of-the-art MKGC methods. Codes are available at https://github.com/OreOZhao/MoSE4MKGC.

MoSE: Modality Split and Ensemble for Multimodal Knowledge Graph Completion

Short text classification (STC) is hard as short texts lack context information and labeled data is not enough. Graph neural networks obtain the state-of-the-art on STC since they can merge various auxiliary information via the message passing framework. However, existing works conduct transductive learning, which requires retraining to accommodate new samples and takes large memory. In this paper, we present SimpleSTC which handles inductive STC problem but only leverages words. We construct word graph from an external large corpus to compensate for the lack of semantic information, and learn text graph to handle the lack of labeled data. Results show that SimpleSTC obtains state-of-the-art performance with lower memory consumption and faster inference speed.

Simplified Graph Learning for Inductive Short Text Classification

We introduce EdgeFormer -- a parameter-efficient Transformer for on-device seq2seq generation under the strict computation and memory constraints. Compared with the previous parameter-efficient Transformers, EdgeFormer applies two novel principles for cost-effective parameterization, allowing it to perform better given the same parameter budget; moreover, EdgeFormer is further enhanced by layer adaptation innovation that is proposed for improving the network with shared layers.

Extensive experiments show EdgeFormer can effectively outperform previous parameter-efficient Transformer baselines and achieve competitive results under both the computation and memory constraints. Given the promising results, we release EdgeLM -- the pretrained version of EdgeFormer, which is the first publicly available pretrained on-device seq2seq model that can be easily fine-tuned for seq2seq tasks with strong results, facilitating on-device seq2seq generation in practice.

EdgeFormer: A Parameter-Efficient Transformer for On-Device Seq2seq Generation

Proof generation focuses on deductive reasoning: given a hypothesis and a set of theories, including some supporting facts and logical rules expressed in natural language, the model generates a proof tree indicating how to deduce the hypothesis from given theories.
Current models with state-of-the-art performance employ the stepwise method that adds an individual node to the proof step-by-step.
However, these methods actually focus on generating several proof paths rather than a whole tree.
During generation, they focus on the most relevant areas of the currently generated node while neglecting the rest of the proof tree. 
To address this problem, we propose ProofInfer, which generates the proof tree via iterative hierarchical inference.
At each step, ProofInfer adds the entire layer to the proof, where all nodes in this layer are generated simultaneously. 
Since the conventional autoregressive generation architecture cannot simultaneously predict multiple nodes, ProofInfer employs text-to-text paradigm.
To this end, we propose a divide-and-conquer algorithm to encode the proof tree as the plain text without losing structure information.
Experimental results show that ProofInfer significantly improves performance on several widely-used datasets.
In addition, ProofInfer still performs well with data-limited, achieving comparable performance to the state-of-the-art model with about 40% of the training data.

ProofInfer: Generating Proof via Iterative Hierarchical Inference

Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue understanding encompasses many diverse tasks, yet task transfer has not been thoroughly studied in conversational AI. This work explores conversational task transfer by introducing FETA: a benchmark for FEw-sample TAsk transfer in open-domain dialogue.
FETA contains two underlying sets of conversations upon which there are 10 and 7 tasks annotated, enabling the study of intra-dataset task transfer; task transfer without domain adaptation. We utilize three popular language models and three learning algorithms to analyze the transferability between 132 source-target task pairs and create a baseline for future work.
We run experiments in the single- and multi-source settings and report valuable findings, e.g., most performance trends are model-specific, and span extraction and multiple-choice tasks benefit the most from task transfer.
In addition to task transfer, FETA can be a valuable resource for future research into the efficiency and generalizability of pre-training datasets and model architectures, as well as for learning settings such as continual and multitask learning.

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