Miracle-dz/UniFAR-FTUs huggingface dataset:
Transfer learning for multilingual vacancy text generation

Writing job vacancies is a repetitive and expensive task for humans. This research focuses on automatically generating the benefit sections of vacancies at redacted from job attributes using mT5, the multilingual version of the state-of-the-art T5 transformer trained on general domains to generate texts in multiple languages. While transformers are accurate at generating coherent text, they are sometimes incorrect at including the structured data (the input) in the generated text. Including the input correctly is crucial for vacancy text generation; otherwise, the candidates may get misled. To evaluate how the model includes the input we developed our own domain-specific metrics (input generation accuracy). This was necessary, because Relation Generation, the pre-existing evaluation metric for data-to-text generation uses only string matching, which was not suitable for our dataset (due to the binary field). With the help of the new evaluation method we were able to measure how well the input is included in the generated text separately for different types of inputs (binary, categorical, numeric), offering another contribution to the field. Additionally, we also evaluated how accurate the mT5 model generates the text in the requested language. The results show that mT5 is very accurate at generating the text in the correct language, at including seen categorical inputs and binary values correctly in the generated text. However, mT5 performed worse when generating text from unseen city names or working with numeric inputs. Furthermore, we found that generating additional synthetic training data for the samples with numeric input can increase the input generation accuracy, however this only works when the numbers are integers and only cover a small range.
A Gamified Approach to Frame Semantic Role Labeling

Much research has investigated the possibility of creating games with a purpose (GWAPs), i.e., online games whose purpose is gathering information to address the insufficient amount of data for training and testing of large language models (Von Ahn and Dabbish, 2008). Based on such work, this paper reports on the development of a game for frame semantic role labeling, where players have fun while using semantic frames as prompts for short story writing. This game will generate more annotations for FrameNet and original content for annotation, supporting FrameNet’s goal of characterizing the English language in terms of Frame Semantics.
Efficient Deep Learning-based Sentence Boundary Detection in Legal Text

A key component of the Natural Language Processing (NLP) pipeline is Sentence Boundary Detection (SBD). Erroneous SBD could affect other processing steps and reduce performance. A few criteria based on punctuation and capitalization are necessary to identify sentence borders in well-defined corpora. However, due to several grammatical ambiguities, the complex structure of legal data poses difficulties for SBD. In this paper, we have trained a neural network framework for identifying the end of the sentence in legal text. We used several state-of-the-art deep learning models, analyzed their performance, and identified that Convolutional Neural Network(CNN) outperformed other deep learning frameworks. We compared the results with rule-based, statistical, and transformer-based frameworks. The best neural network model outscored the popular rule-based framework with an improvement of 8{% in the F1 score. Although domain-specific statistical models have slightly improved performance, the trained CNN is 80 times faster in run-time and doesn’t require much feature engineering. Furthermore, after extensive pretraining, the transformer models fall short in overall performance compared to the best deep learning model.
FigurativeQA: A Test Benchmark for Figurativeness Comprehension for Question Answering

Figurative language is widespread in human language (Lakoff and Johnson, 2008) posing potential challenges in NLP applications. In this paper, we investigate the effect of figurative language on the task of question answering (QA). We construct FigQA, a test set of 400 yes-no questions with figurative and non-figurative contexts, extracted from product reviews and restaurant reviews. We demonstrate that a state-of-the-art RoBERTa QA model has considerably lower performance in question answering when the contexts are figurative rather than literal, indicating a gap in current models. We propose a general method for improving the performance of QA models by converting the figurative contexts into non-figurative by prompting GPT-3, and demonstrate its effectiveness. Our results indicate a need for building QA models infused with figurative language understanding capabilities.
Prompt-Based Meta-Learning For Few-shot Text Classification

Few-shot Text Classification predicts the semantic label of a given text with a handful of supporting instances. Current meta-learning methods have achieved satisfying results in various few-shot situations. Still, they often require a large amount of data to construct many few-shot tasks for meta-training, which is not practical in real-world few-shot scenarios. Prompt-tuning has recently proved to be another effective few-shot learner by bridging the gap between pre-train and downstream tasks. In this work, we closely combine the two promising few-shot learning methodologies in structure and propose a Prompt-Based Meta-Learning (PBML) model to overcome the above meta-learning problem by adding the prompting mechanism. PBML assigns label word learning to base-learners and template learning to meta-learner, respectively. Experimental results show state-of-the-art performance on four text classification datasets under few-shot settings, with higher accuracy and good robustness. We demonstrate through low-resource experiments that our method alleviates the shortcoming that meta-learning requires too much data for meta-training. In the end, we use the visualization to interpret and verify that the meta-learning framework can help the prompting method converge better. We release our code to reproduce our experiments.
Learning Inter-Entity-Interaction for Few-Shot Knowledge Graph Completion

Few-shot knowledge graph completion (FKGC) aims to infer unknown fact triples of a relation using its few-shot reference entity pairs. Recent FKGC studies focus on learning semantic representations of entity pairs by separately encoding the neighborhoods of head and tail entities. Such practice, however, ignores the inter-entity interaction, resulting in low-discrimination representations for entity pairs, especially when these entity pairs are associated with 1-to-N, N-to-1, and N-to-N relations. To address this issue, this paper proposes a novel FKGC model, named Cross-Interaction Attention Network (CIAN) to investigate the inter-entity interaction between head and tail entities. Specifically, we first explore the interactions within entities by computing the attention between the task relation and each entity neighbor, and then model the interactions between head and tail entities by letting an entity to attend to the neighborhood of its paired entity. In this way, CIAN can figure out the relevant semantics between head and tail entities, thereby generating more discriminative representations for entity pairs. Extensive experiments on two public datasets show that CIAN outperforms several state-of-the-art methods. The source code is available at https://github.com/cjlyl/FKGC-CIAN.
Automated Evaluation Metric for Terminology Consistency in MT

The most widely used metrics for machine translation tackle sentence-level evaluation. However, at least for professional domains such as legal texts, it is crucial to measure the consistency of the translation of the terms throughout the whole text. This paper introduces an automated metric for the term consistency evaluation in machine translation (MT). To demonstrate the metric’s performance, we used the Czech-to-English translated texts from the ELITR 2021 agreement corpus and the outputs of the MT systems that took part in WMT21 News Task. We show different modes of our evaluation algorithm and try to interpret the differences in the ranking of the translation systems based on sentence-level metrics and our approach. We also demonstrate that the proposed metric scores significantly differ from the widespread automated metric scores, and correlate with the human assessment.
SRT’s Neural Machine Translation System for WMT22 Biomedical Translation Task

This paper describes the Samsung Research’s Translation system (SRT) submitted to the WMT22 biomedical translation task in two language directions: English to Spanish and Spanish to English. To improve the overall quality, we adopt the deep transformer architecture and employ the back-translation strategy for monolingual corpus. One of the issues in the domain translation is to translate domain-specific terminologies well. To address this issue, we apply the soft-constrained terminology translation based on biomedical terminology dictionaries. In this paper, we provide the performance of our system with WMT20 and WMT21 biomedical testsets. Compared to the best model in WMT20 and WMT21, our system shows equal or better performance. According to the official evaluation results in terms of BLEU scores, our systems get the highest scores in both directions.
Samsung Research Philippines - Datasaur AI’s Submission for the WMT22 Large Scale Multilingual Translation Task

This paper describes the submission of the joint Samsung Research Philippines - Datasaur AI team for the WMT22 Large Scale Multilingual African Translation shared task. We approach the contest as a way to explore task composition as a solution for low-resource multilingual translation, using adapter fusion to combine multiple task adapters that learn subsets of the total translation pairs. Our final model shows performance improvements in 32 out of the 44 translation directions that we participate in when compared to a single model system trained on multiple directions at once.
Theory of helium adsorption on simple and noble-metal surfaces

Abstract : The repulsive part of the helium-metal physisorption potential is calculated in the Hartree-Fock approximation. To the lowest order in the overlap (S) between the atomic and metallic wavefunctions, this interaction is determined by the change in the single-particle density of states of the metal. Combined with a previous calculation of the van der Waals interaction, helium-metal potentials are derived for the simple and noble metals using a jellium model for the metal. Binding energies and equilibrium positions of the helium atom are determined. For the simple metals, the binding energy decreases with increasing r sub s. Its value for the noble metals is between 40 - 70 K. The equilibrium positions are found to be between 3 and 7 A (relative to the jellium background) for all the metals studied. (Author)
Improving Dialogue Act Recognition with Augmented Data

We present our work on augmenting dialog act recognition capabilities utilizing synthetically generated data. Our work is motivated by the limitations of current dialog act datasets, and the need to adapt for new domains as well as ambiguity in utterances written by humans. We list our observations and findings towards how synthetically generated data can contribute meaningfully towards more robust dialogue act recognition models extending to new domains. Our major finding shows that synthetic data, which is linguistically varied, can be very useful towards this goal and increase the performance from (0.39, 0.16) to (0.85, 0.88) for AFFIRM and NEGATE dialog acts respectively.
Should We Ban English NLP for a Year?

Around two thirds of NLP research at top venues is devoted exclusively to developing technology for speakers of English, most speech data comes from young urban speakers, and most texts used to train language models come from male writers. These biases feed into consumer technologies to widen existing inequality gaps, not only within, but also across, societies. Many have argued that it is almost impossible to mitigate inequality amplification. I argue that, on the contrary, it is quite simple to do so, and that counter-measures would have little-to-no negative impact, except for, perhaps, in the very short term.
REUSE: REference-free UnSupervised Quality Estimation Metric

This paper describes our submission to the WMT2022 shared metrics task. Our unsupervised metric estimates the translation quality at chunk-level and sentence-level. Source and target sentence chunks are retrieved by using a multi-lingual chunker. The chunk-level similarity is computed by leveraging BERT contextual word embeddings and sentence similarity scores are calculated by leveraging sentence embeddings of Language-Agnostic BERT models. The final quality estimation score is obtained by mean pooling the chunk-level and sentence-level similarity scores. This paper outlines our experiments and also reports the correlation with human judgements for en-de, en-ru and zh-en language pairs of WMT17, WMT18 and WMT19 test sets.
Food for Thought: How can we exploit contextual embeddings in the translation of idiomatic expressions?

Idiomatic expressions (or idioms) are phrases where the meaning of the phrase cannot be determined from the meaning of the individual words in the expression. Translating idioms between languages is therefore a challenging task. Transformer models based on contextual embeddings have advanced the state-of-the-art across many domains in the field of natural language processing. While research using transformers has advanced both idiom detection as well as idiom disambiguation, idiom translation has not seen a similar advancement. In this work, we investigate two approaches to fine-tuning a pretrained Text-to-Text Transfer Transformer (T5) model to perform idiom translation from English to German. The first approach directly translates English idiom-containing sentences to German, while the second is underpinned by idiom paraphrasing, firstly paraphrasing English idiomatic expressions to their simplified English versions before translating them to German. Results of our evaluation show that each of the approaches is able to generate adequate translations.
“Moments of the Surplus before Ruin and the Deficit at Ruin in the Erlang(2) Risk Process,” Yebin Cheng and Qihe Tang, January 2003

DICKSON, D. C. M. 1998. “On a Class of Renewal Risk Process,” North American Actuarial Journal 2(3): 60–68; Discussions 68–73. DICKSON, D. C. M. , AND C. HIPP. 2001. “On the Time to Ruin for Erlang(2) Risk Process,” Insurance: Mathematics & Economics 29: 333–44. ROSS, S. M. 2000. Introduction to Probability Models, 7th ed. San Diego: Academic Press. SEAL, H. L. 1976. “When Does a Renewal, or Other Stationary Point Process, Start? ” Scandinavian Actuarial Journal 114–17. WILLMOT, G. E. , AND D. C. M. DICKSON. 2003. “The Gerber-Shiu Discounted Penalty Function in the Stationary Renewal Risk Model,” Insurance: Mathematics & Economics. Forthcoming.
Simultaneous Estimation of Speed and Rotor Resistance in Sensorless Induction Motor Vector Controlled Drive

Abstract In this paper a new sensorless indirect vector controlled induction motor drive robust against rotor resistance variation is presented. The speed and rotor resistance are estimated simultaneously, which is reported in many papers as impossible. The estimation is achieved using a reduced order Kalman filter to reduce the computational burden. This algorithm uses a reduced order model of the motor. The model takes into account the coupling between the electrical and mechanical modes, which is true for small size machines. The method proposed in this paper is applicable to a large category of induction motor drives with a gradually varying load torque such as viscous friction, fan/blower and centrifugal pump. A fully real-time digital simulation, a new powerful tool for rapid control prototyping, is carried out to verify the effectiveness of the proposed method. Results show that accurate estimation is achieved under both transient and steady state conditions without injecting any external signal. This achievement is, to the best of authors' knowledge, reported for the first time and is believed to be of great importance for induction machine sensorless control.
Improving compositional generalization for multi-step quantitative reasoning in question answering

Quantitative reasoning is an important aspect of question answering, especially when numeric and verbal cues interact to indicate sophisticated, multi-step programs. In this paper, we demonstrate how modeling the compositional nature of quantitative text can enhance the performance and robustness of QA models, allowing them to capture arithmetic logic that is expressed verbally. Borrowing from the literature on semantic parsing, we propose a method that encourages the QA models to adjust their attention patterns and capture input/output alignments that are meaningful to the reasoning task. We show how this strategy improves program accuracy and renders the models more robust against overfitting as the number of reasoning steps grows. Our approach is designed as a standalone module which can be prepended to many existing models and trained in an end-to-end fashion without the need for additional supervisory signal. As part of this exercise, we also create a unified dataset building on four previously released numerical QA datasets over tabular data.
Investigating the Characteristics of a Transformer in a Few-Shot Setup: Does Freezing Layers in RoBERTa Help?

Transformer based language models have been widely adopted by industrial and research organisations in developing machine learning applications in the presence of limited annotated data. While these models show remarkable results, their functioning in few-shot settings is still poorly understood. Hence, we perform an investigative study to understand the characteristics of such models fine-tuned in few-shot setups. Specifically, we compare the intermediate layer representations obtained from a few-shot model and a pre-trained language model. We observe that pre-trained and few-shot models show similar representations over initial layers, whereas the later layers show a stark deviation. Based on these observations, we propose to freeze the initial Transformer layers to fine-tune the model in a constrained text classification setup with K annotated data points per class, where K ranges from 8 to 64. In our experiments across six benchmark sentence classification tasks, we discover that freezing initial 50% Transformer layers not only reduces training time but also surprisingly improves Macro F1 (upto 8%) when compared to fully trainable layers in few-shot setup. We also observe that this idea of layer freezing can very well be generalized to state-of-the-art few-shot text classification techniques, like DNNC and LM-BFF, leading to significant reduction in training time while maintaining comparable performance.
Offline-to-Online Co-Evolutional User Simulator and Dialogue System

Reinforcement learning (RL) has emerged as a promising approach to fine-tune offline pretrained GPT-2 model in task-oriented dialogue (TOD) systems. In order to obtain human-like online interactions while extending the usage of RL, building pretrained user simulators (US) along with dialogue systems (DS) and facilitating jointly fine-tuning via RL becomes prevalent. However, joint training brings distributional shift problem caused by compounding exposure bias. Existing methods usually iterative update US and DS to ameliorate the ensued non-stationarity problem, which could lead to sub-optimal policy and less sample efficiency. To take a step further for tackling the problem, we introduce an Offline-to-oNline Co-Evolutional (ONCE) framework, which enables bias-aware concurrent joint update for RL-based fine-tuning whilst takes advantages from GPT-2 based end-to-end modeling on US and DS. Extensive experiments demonstrate that ONCE builds high-quality loops of policy learning and dialogues data collection, and achieves state-of-the-art online and offline evaluation results on MultiWOZ2.1 dataset. Opensourced code will be implemented with Mindspore (MS, 2022) and released on our homepage.
Text Simplification for Legal Domain: {I}nsights and Challenges

Legal documents such as contracts contain complex and domain-specific jargons, long and nested sentences, and often present with several details that may be difficult to understand for laypeople without domain expertise.In this paper, we explore the problem of text simplification (TS) in legal domain.The main challenge to this is the lack of availability of complex-simple parallel datasets for the legal domain.We investigate some of the existing datasets, methods, and metrics in the TS literature for simplifying legal texts, and perform human evaluation to analyze the gaps. {footnote{The model outputs and human ratings are available at {url{https://bit.ly/3U3ddIl}. }We present some of the challenges involved, and outline a few open questions that need to be addressed for future research in this direction.
Arabic dialect identification using machine learning and transformer-based models: Submission to the NADI 2022 Shared Task

Arabic has a wide range of dialects. Dialect is the language variation of a specific community. In this paper, we show the models we created to participate in the third Nuanced Arabic Dialect Identification (NADI) shared task (Subtask 1) that involves developing a system to classify a tweet into a country-level dialect. We utilized a number of machine learning techniques as well as deep learning transformer-based models. For the machine learning approach, we build an ensemble classifier of various machine learning models. In our deep learning approach, we consider bidirectional LSTM model and AraBERT pretrained model. The results demonstrate that the deep learning approach performs noticeably better than the other machine learning approaches with 68.7% accuracy on the development set.
BioReader: a Retrieval-Enhanced Text-to-Text Transformer for Biomedical Literature

The latest batch of research has equipped language models with the ability to attend over relevant and factual information from non-parametric external sources, drawing a complementary path to architectural scaling. Besides mastering language, exploiting and contextualizing the latent world knowledge is crucial in complex domains like biomedicine. However, most works in the field rely on general-purpose models supported by databases like Wikipedia and Books. We introduce BioReader, the first retrieval-enhanced text-to-text model for biomedical natural language processing. Our domain-specific T5-based solution augments the input prompt by fetching and assembling relevant scientific literature chunks from a neural database with ≈60 million tokens centered on PubMed. We fine-tune and evaluate BioReader on a broad array of downstream tasks, significantly outperforming several state-of-the-art methods despite using up to 3x fewer parameters. In tandem with extensive ablation studies, we show that domain knowledge can be easily altered or supplemented to make the model generate correct predictions bypassing the retraining step and thus addressing the literature overload issue.