ymoslem/acl-abstracts huggingface dataset:
Toward Creation of Ancash Lexical Resources from OCR (2021)

The Quechua linguistic family has a limited number of NLP resources, most of them being dedicated to Southern Quechua, whereas the varieties of Central Quechua have, to the best of our knowledge, no specific resources (software, lexicon or corpus). Our work addresses this issue by producing two resources for the Ancash Quechua: a full digital version of a dictionary, and an OCR model adapted to the considered variety. In this paper, we describe the steps towards this goal: we first measure performances of existing models for the task of digitising a Quechua dictionary, then adapt a model for the Ancash variety, and finally create a reliable resource for NLP in XML-TEI format. We hope that this work will be a basis for initiating NLP projects for Central Quechua, and that it will encourage digitisation initiatives for under-resourced languages.
Ayuuk-Spanish Neural Machine Translator (2021)

This paper presents the first neural machine translator system for the Ayuuk language. In our experiments we translate from Ayuuk to Spanish, and fromSpanish to Ayuuk. Ayuuk is a language spoken in the Oaxaca state of Mexico by the Ayuukj{\"a}'{\"a}y people (in Spanish commonly known as Mixes. We use different sources to create a low-resource parallel corpus, more than 6,000 phrases. For some of these resources we rely on automatic alignment. The proposed system is based on the Transformer neural architecture and it uses sub-word level tokenization as the input. We show the current performance given the resources we have collected for the San Juan G{\"u}ichicovi variant, they are promising, up to 5 BLEU. We based our development on the Masakhane project for African languages.
Explicit Tone Transcription Improves ASR Performance in Extremely Low-Resource Languages: A Case Study in Bribri (2021)

Linguistic tone is transcribed for input into ASR systems in numerous ways. This paper shows a systematic test of several transcription styles, using as an example the Chibchan language Bribri, an extremely low-resource language from Costa Rica. The most successful models separate the tone from the vowel, so that the ASR algorithms learn tone patterns independently. These models showed improvements ranging from 4{\%} to 25{\%} in character error rate (CER), and between 3{\%} and 23{\%} in word error rate (WER). This is true for both traditional GMM/HMM and end-to-end CTC algorithms. This paper also presents the first attempt to train ASR models for Bribri. The best performing models had a CER of 33{\%} and a WER of 50{\%}. Despite the disadvantage of using hand-engineered representations, these models were trained on only 68 minutes of data, and therefore show the potential of ASR to generate further training materials and aid in the documentation and revitalization of the language.
Towards a morphological transducer and orthography converter for Western Tlacolula Valley Zapotec (2021)

This paper presents work towards a morphological transducer and orthography converter for Dizhsa, or San Lucas Quiavin{\'i} Zapotec, an endangered Western Tlacolula Valley Zapotec language. The implementation of various aspects of the language`s morphology is presented, as well as the transducer`s ability to perform analysis in two orthographies and convert between them. Potential uses of the transducer for language maintenance and issues of licensing are also discussed. Evaluation of the transducer shows that it is fairly robust although incomplete, and evaluation of orthographic conversion shows that this method is strongly affected by the coverage of the transducer.
Peru is Multilingual, Its Machine Translation Should Be Too? (2021)

Peru is a multilingual country with a long history of contact between the indigenous languages and Spanish. Taking advantage of this context for machine translation is possible with multilingual approaches for learning both unsupervised subword segmentation and neural machine translation models. The study proposes the first multilingual translation models for four languages spoken in Peru: Aymara, Ashaninka, Quechua and Shipibo-Konibo, providing both many-to-Spanish and Spanish-to-many models and outperforming pairwise baselines in most of them. The task exploited a large English-Spanish dataset for pre-training, monolingual texts with tagged back-translation, and parallel corpora aligned with English. Finally, by fine-tuning the best models, we also assessed the out-of-domain capabilities in two evaluation datasets for Quechua and a new one for Shipibo-Konibo.
Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas (2021)

This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas. The shared task featured two independent tracks, and participants submitted machine translation systems for up to 10 indigenous languages. Overall, 8 teams participated with a total of 214 submissions. We provided training sets consisting of data collected from various sources, as well as manually translated sentences for the development and test sets. An official baseline trained on this data was also provided. Team submissions featured a variety of architectures, including both statistical and neural models, and for the majority of languages, many teams were able to considerably improve over the baseline. The best performing systems achieved 12.97 ChrF higher than baseline, when averaged across languages.
Open Machine Translation for Low Resource South American Languages (AmericasNLP 2021 Shared Task Contribution) (2021)

This paper describes the team ({\textquotedblleft}Tamalli{\textquotedblright})`s submission to AmericasNLP2021 shared task on Open Machine Translation for low resource South American languages. Our goal was to evaluate different Machine Translation (MT) techniques, statistical and neural-based, under several configuration settings. We obtained the second-best results for the language pairs {\textquotedblleft}Spanish-Bribri{\textquotedblright}, {\textquotedblleft}Spanish-Ash{\'a}ninka{\textquotedblright}, and {\textquotedblleft}Spanish-Rar{\'a}muri{\textquotedblright} in the category {\textquotedblleft}Development set not used for training{\textquotedblright}. Our performed experiments will serve as a point of reference for researchers working on MT with low-resource languages.
NRC-CNRC Machine Translation Systems for the 2021 AmericasNLP Shared Task (2021)

We describe the NRC-CNRC systems submitted to the AmericasNLP shared task on machine translation. We submitted systems translating from Spanish into Wix{\'a}rika, Nahuatl, Rar{\'a}muri, and Guaran{\'i}. Our best neural machine translation systems used multilingual pretraining, ensembling, finetuning, training on parts of the development data, and subword regularization. We also submitted translation memory systems as a strong baseline.
Low-Resource Machine Translation Using Cross-Lingual Language Model Pretraining (2021)

This paper describes UTokyo`s submission to the AmericasNLP 2021 Shared Task on machine translation systems for indigenous languages of the Americas. We present a low-resource machine translation system that improves translation accuracy using cross-lingual language model pretraining. Our system uses an mBART implementation of fairseq to pretrain on a large set of monolingual data from a diverse set of high-resource languages before finetuning on 10 low-resource indigenous American languages: Aymara, Bribri, Ash{\'a}ninka, Guaran{\'i}, Wixarika, N{\'a}huatl, H{\~n}{\"a}h{\~n}u, Quechua, Shipibo-Konibo, and Rar{\'a}muri. On average, our system achieved BLEU scores that were 1.64 higher and chrF scores that were 0.0749 higher than the baseline.
The REPU CS' Spanish--Quechua Submission to the AmericasNLP 2021 Shared Task on Open Machine Translation (2021)

We present the submission of REPUcs to the AmericasNLP machine translation shared task for the low resource language pair Spanish{--}Quechua. Our neural machine translation system ranked first in Track two (development set not used for training) and third in Track one (training includes development data). Our contribution is focused on: (i) the collection of new parallel data from different web sources (poems, lyrics, lexicons, handbooks), and (ii) using large Spanish{--}English data for pre-training and then fine-tuning the Spanish{--}Quechua system. This paper describes the new parallel corpora and our approach in detail.
Moses and the Character-Based Random Babbling Baseline: CoAStaL at AmericasNLP 2021 Shared Task (2021)

We evaluated a range of neural machine translation techniques developed specifically for low-resource scenarios. Unsuccessfully. In the end, we submitted two runs: (i) a standard phrase-based model, and (ii) a random babbling baseline using character trigrams. We found that it was surprisingly hard to beat (i), in spite of this model being, in theory, a bad fit for polysynthetic languages; and more interestingly, that (ii) was better than several of the submitted systems, highlighting how difficult low-resource machine translation for polysynthetic languages is.
The Helsinki submission to the AmericasNLP shared task (2021)

The University of Helsinki participated in the AmericasNLP shared task for all ten language pairs. Our multilingual NMT models reached the first rank on all language pairs in track 1, and first rank on nine out of ten language pairs in track 2. We focused our efforts on three aspects: (1) the collection of additional data from various sources such as Bibles and political constitutions, (2) the cleaning and filtering of training data with the OpusFilter toolkit, and (3) different multilingual training techniques enabled by the latest version of the OpenNMT-py toolkit to make the most efficient use of the scarce data. This paper describes our efforts in detail.
IndT5: A Text-to-Text Transformer for 10 Indigenous Languages (2021)

Transformer language models have become fundamental components of NLP based pipelines. Although several Transformer have been introduced to serve many languages, there is a shortage of models pre-trained for low-resource and Indigenous languages in particular. In this work, we introduce IndT5, the first Transformer language model for Indigenous languages. To train IndT5, we build IndCorpus, a new corpus for 10 Indigenous languages and Spanish. We also present the application of IndT5 to machine translation by investigating different approaches to translate between Spanish and the Indigenous languages as part of our contribution to the AmericasNLP 2021 Shared Task on Open Machine Translation. IndT5 and IndCorpus are publicly available for research.
Feature-level Incongruence Reduction for Multimodal Translation (2021)

Caption translation aims to translate image annotations (captions for short). Recently, Multimodal Neural Machine Translation (MNMT) has been explored as the essential solution. Besides of linguistic features in captions, MNMT allows visual(image) features to be used. The integration of multimodal features reinforces the semantic representation and considerably improves translation performance. However, MNMT suffers from the incongruence between visual and linguistic features. To overcome the problem, we propose to extend MNMT architecture with a harmonization network, which harmonizes multimodal features(linguistic and visual features)by unidirectional modal space conversion. It enables multimodal translation to be carried out in a seemingly monomodal translation pipeline. We experiment on the golden Multi30k-16 and 17. Experimental results show that, compared to the baseline,the proposed method yields the improvements of 2.2{\%} BLEU for the scenario of translating English captions into German (En{\textrightarrow}De) at best,7.6{\%} for the case of English-to-French translation(En{\textrightarrow}Fr) and 1.5{\%} for English-to-Czech(En{\textrightarrow}Cz). The utilization of harmonization network leads to the competitive performance to the-state-of-the-art.
Error Causal inference for Multi-Fusion models (2021)

In this paper, we propose an error causal inference method that could be used for finding dominant features for a faulty instance under a well-trained multi-modality input model, which could apply to any testing instance. We evaluate our method using a well-trained multi-modalities stylish caption generation model and find those causal inferences that could provide us the insights for next step optimization.
Leveraging Partial Dependency Trees to Control Image Captions (2021)

Controlling the generation of image captions attracts lots of attention recently. In this paper, we propose a framework leveraging partial syntactic dependency trees as control signals to make image captions include specified words and their syntactic structures. To achieve this purpose, we propose a Syntactic Dependency Structure Aware Model (SDSAM), which explicitly learns to generate the syntactic structures of image captions to include given partial dependency trees. In addition, we come up with a metric to evaluate how many specified words and their syntactic dependencies are included in generated captions. We carry out experiments on two standard datasets: Microsoft COCO and Flickr30k. Empirical results show that image captions generated by our model are effectively controlled in terms of specified words and their syntactic structures. The code is available on GitHub.
Grounding Plural Phrases: Countering Evaluation Biases by Individuation (2021)

Phrase grounding (PG) is a multimodal task that grounds language in images. PG systems are evaluated on well-known benchmarks, using Intersection over Union (IoU) as evaluation metric. This work highlights a disconcerting bias in the evaluation of grounded plural phrases, which arises from representing sets of objects as a union box covering all component bounding boxes, in conjunction with the IoU metric. We detect, analyze and quantify an evaluation bias in the grounding of plural phrases and define a novel metric, c-IoU, based on a union box`s component boxes. We experimentally show that our new metric greatly alleviates this bias and recommend using it for fairer evaluation of plural phrases in PG tasks.
PanGEA: The Panoramic Graph Environment Annotation Toolkit (2021)

PanGEA, the Panoramic Graph Environment Annotation toolkit, is a lightweight toolkit for collecting speech and text annotations in photo-realistic 3D environments. PanGEA immerses annotators in a web-based simulation and allows them to move around easily as they speak and/or listen. It includes database and cloud storage integration, plus utilities for automatically aligning recorded speech with manual transcriptions and the virtual pose of the annotators. Out of the box, PanGEA supports two tasks {--} collecting navigation instructions and navigation instruction following {--} and it could be easily adapted for annotating walking tours, finding and labeling landmarks or objects, and similar tasks. We share best practices learned from using PanGEA in a 20,000 hour annotation effort to collect the Room-Across-Room dataset. We hope that our open-source annotation toolkit and insights will both expedite future data collection efforts and spur innovation on the kinds of grounded language tasks such environments can support.
Learning to Learn Semantic Factors in Heterogeneous Image Classification (2021)

Few-shot learning is to recognize novel classes with a few labeled samples per class. Although numerous meta-learning methods have made significant progress, they struggle to directly address the heterogeneity of training and evaluating task distributions, resulting in the domain shift problem when transitioning to new tasks with disjoint spaces. In this paper, we propose a novel method to deal with the heterogeneity. Specifically, by simulating class-difference domain shift during the meta-train phase, a bilevel optimization procedure is applied to learn a transferable representation space that can rapidly adapt to heterogeneous tasks. Experiments demonstrate the effectiveness of our proposed method.
Reference and coreference in situated dialogue (2021)

In recent years several corpora have been developed for vision and language tasks. We argue that there is still significant room for corpora that increase the complexity of both visual and linguistic domains and which capture different varieties of perceptual and conversational contexts. Working with two corpora approaching this goal, we present a linguistic perspective on some of the challenges in creating and extending resources combining language and vision while preserving continuity with the existing best practices in the area of coreference annotation.
Findings on Conversation Disentanglement (2021)

Conversation disentanglement, the task to identify separate threads in conversations, is an important pre-processing step in multi-party conversational NLP applications such as conversational question answering and con-versation summarization. Framing it as a utterance-to-utterance classification problem {\^a} i.e. given an utterance of interest (UOI), find which past utterance it replies to {\^a} we explore a number of transformer-based models and found that BERT in combination with handcrafted features remains a strong baseline. We then build a multi-task learning model that jointly learns utterance-to-utterance and utterance-to-thread classification. Observing that the ground truth label (past utterance) is in the top candidates when our model makes an error, we experiment with using bipartite graphs as a post-processing step to learn how to best match a set of UOIs to past utterances. Experiments on the Ubuntu IRC dataset show that this approach has the potential to out-perform the conventional greedy approach of simply selecting the highest probability candidate for each UOI independently, indicating a promising future research direction.
An Approach to the Frugal Use of Human Annotators to Scale up Auto-coding for Text Classification Tasks (2021)

Human annotation for establishing the training data is often a very costly process in natural language processing (NLP) tasks, which has led to frugal NLP approaches becoming an important research topic. Many research teams struggle to complete projects with limited funding, labor, and computational resources. Driven by the Move-Step analytic framework theorized in the applied linguistics field, our study offers a rigorous approach to the frugal use of two human annotators to scale up auto-coding for text classification tasks. We applied the Linear Support Vector Machine algorithm to text classification of a job ad corpus. Our Cohen{\^a}s Kappa for inter-rater agreement and Area Under the Curve (AUC) values reached averages of 0.76 and 0.80, respectively. The calculated time consumption for our human training process was 36 days. The results indicated that even the strategic and frugal use of only two human annotators could enable the efficient training of classifiers with reasonably good performance. This study does not aim to provide generalizability of the results. Rather, we propose that the annotation strategies arising from this study be considered by our readers only if such strategies are fit for one`s specific research purposes.
Curriculum Learning Effectively Improves Low Data VQA (2021)

Visual question answering (VQA) models, in particular modular ones, are commonly trained on large-scale datasets to achieve state-of-the-art performance. However, such datasets are sometimes not available. Further, it has been shown that training these models on small datasets significantly reduces their accuracy. In this paper, we propose curriculum-based learning (CL) regime to increase the accuracy of VQA models trained on small datasets. Specifically, we offer three criteria to rank the samples in these datasets and propose a training strategy for each criterion. Our results show that, for small datasets, our CL approach yields more accurate results than those obtained when training with no curriculum.
Using Word Embeddings to Quantify Ethnic Stereotypes in 12 years of Spanish News (2021)

The current study provides a diachronic analysis of the stereotypical portrayals concerning seven of the most prominent foreign nationalities living in Spain in a Spanish news outlet. We use 12 years (2007-2018) of news articles to train word embedding models to quantify the association of such outgroups with drug use, prostitution, crimes, and poverty concepts. Then, we investigate the effects of sociopolitical variables on the computed bias series, such as the outgroup size in the host country and the rate of the population receiving unemployment benefits. Our findings indicate that the texts exhibit bias against foreign-born people, especially in the case of outgroups for which the country of origin has a lower Gross Domestic Product per capita (PPP) than Spain.
Multi-modal Intent Classification for Assistive Robots with Large-scale Naturalistic Datasets (2021)

Recent years have brought a tremendous growth in assistive robots/prosthetics for people with partial or complete loss of upper limb control. These technologies aim to help the users with various reaching and grasping tasks in their daily lives such as picking up an object and transporting it to a desired location; and their utility critically depends on the ease and effectiveness of communication between the user and robot. One of the natural ways of communicating with assistive technologies is through verbal instructions. The meaning of natural language commands depends on the current configuration of the surrounding environment and needs to be interpreted in this multi-modal context, as accurate interpretation of the command is essential for a successful execution of the user{\^a}s intent by an assistive device. The research presented in this paper demonstrates how large-scale situated natural language datasets can support the development of robust assistive technologies. We leveraged a navigational dataset comprising {\ensuremath{>}}25k human-provided natural language commands covering diverse situations. We demonstrated a way to extend the dataset in a task-informed way and use it to develop multi-modal intent classifiers for pick and place tasks. Our best classifier reached {\ensuremath{>}}98{\%} accuracy in a 16-way multi-modal intent classification task, suggesting high robustness and flexibility.
Harnessing Privileged Information for Hyperbole Detection (2021)

The detection of hyperbole is an important stepping stone to understanding the intentions of a hyperbolic utterance. We propose a model that combines pre-trained language models with privileged information for the task of hyperbole detection. We also introduce a suite of behavioural tests to probe the capabilities of hyperbole detection models across a range of hyperbole types. Our experiments show that our model improves upon baseline models on an existing hyperbole detection dataset. Probing experiments combined with analysis using local linear approximations (LIME) show that our model excels at detecting one particular type of hyperbole. Further, we discover that our experiments highlight annotation artifacts introduced through the process of literal paraphrasing of hyperbole. These annotation artifacts are likely to be a roadblock to further improvements in hyperbole detection.