Korea, Republic of

Language-agnostic sentence embeddings generated by pre-trained models such as LASER and LaBSE are attractive options for mining large datasets to produce parallel corpora for low-resource machine translation. We test LASERand LaBSE in extracting bitext for two related low-resource African languages: Luhya and Swahili. For this work, we created a new parallel set of nearly 8000 Luhya-English sentences, allowing a new zero-shot test of LASER and LaBSE. We find that LaBSE significantly outperforms LASER in both languages. Both LASER and LaBSE however perform poorly at zero-shot alignment on Luhya, achieving just 1.5% and 22.0% successful alignments respectively (P@1 score). We fine-tune the embeddings on a small set of parallel Luhya sentences and show significant gains, improving the LaBSE alignment accuracy to 53.3%. Further, restricting the dataset to sentence embedding pairs with cosine similarity above 0.7 yielded alignments with over 85% accuracy.

COLING 2022

Very Low Resource Sentence Alignment: Luhya and Swahili

labse

laser

sentence alignment

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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ACL 2022

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### THE CONFERENCE WE KNOW AND WE WANT.
**COLING**, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

First established in 1965, the biennial COLING conference is held in diverse parts of the globe and attracts participants from both top-ranked research centers and emerging countries. Today, the most important developments in our field are taking place not only in universities and academic research institutes but also in industrial research departments including tech-startups. COLING provides opportunities for all these communities to showcase their exciting discovery.

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COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

Neural Machine Translation (NMT) models are strong enough to convey semantic and syn- tactic information from the source language to the target language. However, these models are suffering from the need for a large amount of data to learn the parameters. As a result, for languages with scarce data, these models are at risk of underperforming. We propose to augment attention based neural network with reordering information to alleviate the lack of data. This augmentation improves the trans- lation quality for both English to Persian and Persian to English by up to 6% BLEU absolute over the baseline models.


A Preordered RNN Layer Boosts Neural Machine Translation in Low Resource Settings

This paper presents a series of experiments on translating between spoken Spanish and Spanish Sign Language glosses (LSE), including enriching Neural Machine Translation (NMT) systems with linguistic features, and creating synthetic data to pretrain and later on finetune a neural translation model.
We found evidence that pretraining over a large corpus of LSE synthetic data aligned to Spanish sentences could markedly improve the performance of the translation models.

Translating Spanish into Spanish Sign Language: Combining Rules and Data-driven approaches

The development of machine translation (MT) has been successful in breaking the language barrier of the world’s top 10-20 languages. However, for the rest of it, delivering an accept- able translation quality is still a challenge due to the limited resource. To tackle this problem, most studies focus on augmenting data while overlooking the fact that we can “borrow” high- quality natural data from the closely-related language. In this work, we propose an MT model training strategy by increasing the lan- guage directions as a means of augmentation in a multilingual setting. Our experiment result using Indonesian and Malaysian on the state-of- the-art MT model showcases the effectiveness and robustness of our method.

Benefiting from Language Similarity in the Multilingual MT Training: Case Study of Indonesian and Malaysian

Numerous machine translation systems have been proposed since the appearance of this task. Nowadays, new large language model- based algorithms show results that sometimes overcome human ones on the rich-resource lan- guages. Nevertheless, it is still not the case for the low-resource languages, for which all these algorithms did not show equally impres- sive results. In this work, we want to compare 3 generations of machine translation models on 7 low-resource languages and make a step further by proposing a new way of automatic parallel data augmentation using the state-of- the-art generative model.

The only chance to understand: machine translation of the severely endangered low-resource languages of Eurasia

In machine translation, a pivot language can be used to assist the source to target translation model. In pivot-based transfer learning, the source to pivot and the pivot to target models are used to improve the performance of the source to target model. This technique works best when both source-pivot and pivot-target are high resource language pairs and the source-target is a low resource language pair. But in some cases, such as Indic languages, the pivot to target language pair is not a high resource one. To overcome this limitation, we use multiple related languages as pivot languages to assist the source to target model. We show that using multiple pivot languages gives 2.03 BLEU and 3.05 chrF score improvement over the baseline model. We show that strategic decoder initialization while performing pivot-based transfer learning with multiple pivot languages gives a 3.67 BLEU and 5.94 chrF score improvement over the baseline model.

Multiple Pivot Languages and Strategic Decoder Initialization helps Neural Machine Translation

In this presentation, we introduce the pipeline of creating a personalized dialogue dataset using GPT-3. Our pipeline consists of three main parts: (1) ProfileGen creation, (2) Persona Set creation using CoNL, and (3) PersonaChatGen creation. Through experiments, we found that GPT-3 can generate meaningful results which leads to improving the performance compared to the baseline.

PersonaChatGen: Generating Personalized Dialogues using GPT-3

Despite achieving state-of-the-art zero-shot performance, existing vision-language models still fall short of few-shot transfer ability on domain-specific problems. Classical fine-tuning often fails to prevent highly expressive models from exploiting spurious correlations. Although model-agnostic meta-learning (MAML) presents as a natural alternative for few-shot transfer learning, the expensive computation due to implicit second-order optimization limits its use on large-scale vision-language models such as CLIP. While much literature has been devoted to exploring alternative optimization strategies, we identify another essential aspect towards effective few-shot transfer learning, task sampling, which is previously only be viewed as part of data pre-processing in MAML. To show the impact of task sampling, we propose a simple algorithm, Model-Agnostic Multitask Fine-tuning (MAMF), which differentiates classical fine-tuning only on uniformly sampling multiple tasks. Despite its simplicity, we show that MAMF consistently outperforms classical fine-tuning on five few-shot image classification tasks. We further show that the effectiveness of the bi-level optimization in MAML is highly sensitive to the zero-shot performance of a task in the context of few-shot vision-language classification. The goal of this paper is to provide new insights on what makes few-shot learning work, and encourage more research into investigating better task sampling strategies.

Rethinking Task Sampling for Few-shot Vision-Language Transfer Learning

Language education has been shown to benefit from computational argumentation, for example, from methods that assess quality dimensions of language learners' argumentative essays, such as their organization and argument strength. So far, however, little attention has been paid to cultural differences in learners' argument structures originating from different origins and language capabilities. This paper extends prior studies of learner argumentation by analyzing differences in the argument structure of essays from culturally diverse learners. Based on the ICLE corpus containing essays written by English learners of 16 different mother tongues, we train natural language processing models to mine argumentative discourse units (ADUs) as well as to assess the essays' quality in terms of organization and argument strength. The extracted ADUs and the predicted quality scores enable us to look into the similarities and differences of essay argumentation across different English learners. In particular, we analyze the ADUs from learners with different mother tongues, different levels of arguing proficiency, and different context cultures.

Analyzing Culture-Specific Argument Structures in Learner Essays

Identifying claims in text is a crucial first step in argument mining. In this paper, we investigate factors for the composition of training corpora to improve cross-domain claim detection. To this end, we use four recent argumentation corpora annotated with claims and submit them to several experimental scenarios. Our results
indicate that the "ideal" composition of training corpora is characterized by a large corpus size, homogeneous claim proportions, and less formal text domains.

On Selecting Training Corpora for Cross-Domain Claim Detection

We develop a novel unified representation for the argumentation mining task facilitating the extracting from text and the labelling of the non-argumentative units and argumentation components---premises, claims, and major claims---and the argumentative relations---premise to claim or premise in a support or attack relation, and claim to major-claim in a for or against relation---in an end-to-end machine learning pipeline. This tightly integrated representation combines the component and relation identification sub-problems and enables a unitary solution for detecting argumentation structures. This new representation together with a new deep learning architecture composed of a mixed embedding method, a multi-head attention layer, two biLSTM layers, and a final linear layer obtain state-of-the-art accuracy on the Persuasive Essays dataset. Also, we have introduced a decoupled solution to identify the entities and relations first, and on top of that a second model is used to detect distance between the detected related components. An augmentation of the corpus (paragraph version) by including copies of major claims has further increased the performance.

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Very Low Resource Sentence Alignment: Luhya and Swahili

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A Preordered RNN Layer Boosts Neural Machine Translation in Low Resource Settings

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