Taiwan

Machine translation traditionally refers to translating from a single source language into a single target language. In recent years, the field has moved towards large neural models translating from or into many languages. As the input and output languages vary, the model must be correctly cued to translate into the correct target language. This is typically done by prefixing language tokens onto the source or target sequence. A single token’s content can denote the source language, target language, or language pair. The location and content of the prefix varies and many approaches exist without much justification towards one method or another. As guidance to researchers and directions for future work, we present a series of comprehensive experiments that show how the positioning and type of a target language prefix token affects translation performance. We show that source-side prefixes consistently improve performance. Further, we find that best language token content varies dependent on the supported language set.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.19/)

IJCNLP-AACL 2022

The Effects of Language Token Prefixing for Multilingual Machine Translation

language tokens

prefixing

multilingual models

multilingual

machine translation

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

poster

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP).

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Registration Is Required

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.1/)

Transfer Learning for Humor Detection by Twin Masked Yellow Muppets

We build a dual-way neural dictionary to retrieve words given definitions, and produce definitions for queried words. The model learns the two tasks simultaneously and handles unknown words via embeddings. It casts a word or a definition to the same representation space through a shared layer, then generates the other form in a multi-task fashion. Our method achieves promising automatic scores on previous benchmarks without extra resources. Human annotators prefer the model's outputs in both reference-less and reference-based evaluation, indicating its practicality. Analysis suggests that multiple objectives benefit learning.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.2/)

A Unified Model for Reverse Dictionary and Definition Modelling

Previous studies on neural linguistic steganography, except Ueoka et al. (2021), overlook the fact that the sender must detokenize cover texts to avoid arousing the eavesdropper’s suspicion. In this paper, we demonstrate that segmentation
ambiguity indeed causes occasional decoding failures at the receiver’s side. With the nearubiquity of subwords, this problem now affects any language. We propose simple tricks to overcome this problem, which are even applicable to languages without explicit word boundaries.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.15/)

Addressing Segmentation Ambiguity in Neural Linguistic Steganography

The Transformer Language Model is a powerful tool that has been shown to excel at various NLP tasks and has become the de-facto standard solution thanks to its versatility. In this study, we employ pre-trained transformer document embeddings in an Active Learning task to group samples with the same labels in the embedding space on domain-specific corpora. We find that the calculated class embeddings are not close to the respective samples and consequently do not partition the embedding space in a meaningful way. In addition, using the class embeddings as an Active Learning strategy yields reduced results compared to all baselines.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.45/)

Pre-Trained Sentence-BERT does not represent Class Embeddings in Active Learning for Multi-Label Text Classification

Conversational question answering (CQA) facilitates an incremental and interactive understanding of a given context, but building a CQA system is difficult for many domains due to the problem of data scarcity. In this paper, we introduce a novel method to synthesize data for CQA with various question types, including open-ended, closed-ended, and unanswerable questions. We design a different generation flow for each question type and effectively combine them in a single, shared framework. Moreover, we devise a hierarchical answerability classification (hierarchical AC) module that improves quality of the synthetic data while acquiring unanswerable questions.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.22/)

Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions

This paper introduces a first step towards creating the NERDz dataset. A manually annotated dataset of named entities for the Algerian vernacular dialect. The annotations are built on top of a recent extension to the Algerian NArabizi Treebank, comprizing NArabizi sentences with manual transliterations into Arabic and code-switched scripts. NERDz is therefore not only the first dataset of named entities for Algerian, but it also comprises parallel entities written in Latin, Arabic, and code-switched scripts. We present a detailed overview of our annotations, inter-annotator agreement measures, and define two preliminary baselines using a neural sequence labeling approach and an Algerian BERT model. We also make the annotation guidelines and the annotations available for future work.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.13/)

NERDz: A Preliminary Dataset of Named Entities for Algerian

For low-resourced Bangla language, works on detecting emotions on textual data suffer from size and cross-domain adaptability. In our paper, we propose a manually annotated dataset of 22,698 Bangla public comments from social
media sites covering 12 different domains such as Personal, Politics, and Health, labeled for 6 fine-grained emotion categories of the Junto
Emotion Wheel. We invest efforts in the data preparation to 1) preserve the linguistic richness and 2) challenge any classification model. Our experiments to develop a benchmark classification system show that random baselines perform better than neural networks and pre-trained language models as hand-crafted features provide superior performance.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.17/)

EmoNoBa: A Dataset for Analyzing Fine-Grained Emotions on Noisy Bangla Texts

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.5/)

CLIP4IDC: CLIP for Image Difference Captioning

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.40/)

Extractive Entity-Centric Summarization as Sentence Selection using Bi-Encoders

We introduce fastcoref, a python package for fast, accurate, and easy-to-use English coreference resolution. The package is pip-installable, and allows two modes: an accurate mode based on the LingMess architecture, providing state-of-the-art coreference accuracy, and a substantially faster model, F-COREF which is the focus of this work. F-COREF allows to process 2.8K OntoNotes documents in 25 seconds on a V100 GPU (compared to 6 minutes for the LingMess model, and to 12 minutes of the popular AllenNLP coreference model) with only a modest drop in accuracy.
The fast speed is achieved through a combination of distillation of a compact model from the LingMess model, and an efficient batching implementation using a technique we call leftover batching. 

https://github.com/shon-otmazgin/fastcoref

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-demo.6/)


The Effects of Language Token Prefixing for Multilingual Machine Translation

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