Reviewerly/exHarmony huggingface dataset:
Joint Word and Morpheme Segmentation with Bayesian Non-Parametric Models

Language documentation often requires segmenting transcriptions of utterances collected on the field into words and morphemes. While these two tasks are typically performed in succession, we study here Bayesian models for simultaneously segmenting utterances at these two levels. Our aim is twofold: (a) to study the effect of explicitly introducing a hierarchy of units in joint segmentation models; (b) to further assess whether these two levels can be better identified through weak supervision. For this, we first consider a deterministic coupling between independent models; then design and evaluate hierarchical Bayesian models. Experiments with two under-resourced languages (Japhug and Tsez) allow us to better understand the value of various types of weak supervision. In our analysis, we use these results to revisit the distributional hypotheses behind Bayesian segmentation models and evaluate their validity for language documentation data.
MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data

Transformer-based models for question answering (QA) over tables and texts confront a “long” hybrid sequence over tabular and textual elements, causing long-range reasoning problems. To handle long-range reasoning, we extensively employ a fusion-in-decoder (FiD) and exponential moving average (EMA), proposing a Moving Average Equipped Fusion-in-Decoder (MAFiD). With FiD as the backbone architecture, MAFiD combines various levels of reasoning: independent encoding of homogeneous data and single-row and multi-row heterogeneous reasoning, using a gated cross attention layer to effectively aggregate the three types of representations resulting from various reasonings. Experimental results on HybridQA indicate that MAFiD achieves state-of-the-art performance by increasing exact matching (EM) and F1 by 1.1 and 1.7, respectively, on the blind test set.
Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models

Recent transformer language models achieve outstanding results in many natural language processing (NLP) tasks. However, their enormous size often makes them impractical on memory-constrained devices, requiring practitioners to compress them to smaller networks. In this paper, we explore offline compression methods, meaning computationally-cheap approaches that do not require further fine-tuning of the compressed model. We challenge the classical matrix factorization methods by proposing a novel, better-performing autoencoder-based framework. We perform a comprehensive ablation study of our approach, examining its different aspects over a diverse set of evaluation settings. Moreover, we show that enabling collaboration between modules across layers by compressing certain modules together positively impacts the final model performance. Experiments on various NLP tasks demonstrate that our approach significantly outperforms commonly used factorization-based offline compression methods.
Neural Ranking with Weak Supervision for Open-Domain Question Answering : A Survey

Neural ranking (NR) has become a key component for open-domain question-answering in order to access external knowledge. However, training a good NR model requires substantial amounts of relevance annotations, which is very costly to scale. To address this, a growing body of research works have been proposed to reduce the annotation cost by training the NR model with weak supervision (WS) instead. These works differ in what resources they require and employ a diverse set of WS signals to train the model. Understanding such differences is crucial for choosing the right WS technique. To facilitate this understanding, we provide a structured overview of standard WS signals used for training a NR model. Based on their required resources, we divide them into three main categories: (1) only documents are needed; (2) documents and questions are needed; and (3) documents and question-answer pairs are needed. For every WS signal, we review its general idea and choices. Promising directions are outlined for future research.
A Federated Approach for Hate Speech Detection

Hate speech detection has been the subject of high research attention, due to the scale of content created on social media. In spite of the attention and the sensitive nature of the task, privacy preservation in hate speech detection has remained under-studied.The majority of research has focused on centralised machine learning infrastructures which risk leaking data. In this paper, we show that using federated machine learning can help address privacy the concerns that are inherent to hate speech detection while obtaining up to 6.81% improvement in terms of F1-score.
Parameter-Efficient Korean Character-Level Language Modeling

Character-level language modeling has been shown empirically to perform well on highly agglutinative or morphologically rich languages while using only a small fraction of the parameters required by (sub)word models. Korean fits nicely into this framework, except that, like other CJK languages, it has a very large character vocabulary of 11,172 unique syllables. However, unlike Japanese Kanji and Chinese Hanzi, each Korean syllable can be uniquely factored into a small set of subcharacters, called jamo. We explore a “three-hot” scheme, where we exploit the decomposability of Korean characters to model at the syllable level but using only jamo-level representations. We find that our three-hot embedding and decoding scheme alleviates the two major issues with prior syllable- and jamo-level models. Namely, it requires fewer than 1% of the embedding parameters of a syllable model, and it does not require tripling the sequence length, as with jamo models. In addition, it addresses a theoretical flaw in a prior three-hot modeling scheme. Our experiments show that, even when reducing the number of embedding parameters by 99.6% (from 11.4M to just 36k), our model suffers no loss in translation quality compared to the baseline syllable model.
StyLEx: Explaining Style Using Human Lexical Annotations

Large pre-trained language models have achieved impressive results on various style classification tasks, but they often learn spurious domain-specific words to make predictions (Hayati et al., 2021). While human explanation highlights stylistic tokens as important features for this task, we observe that model explanations often do not align with them. To tackle this issue, we introduce StyLEx, a model that learns from human annotated explanations of stylistic features and jointly learns to perform the task and predict these features as model explanations. Our experiments show that StyLEx can provide human like stylistic lexical explanations without sacrificing the performance of sentence-level style prediction on both in-domain and out-of-domain datasets. Explanations from StyLEx show significant improvements in explanation metrics (sufficiency, plausibility) and when evaluated with human annotations. They are also more understandable by human judges compared to the widely-used saliency-based explanation baseline.
Document-Level Planning for Text Simplification

Most existing work on text simplification is limited to sentence-level inputs, with attempts to iteratively apply these approaches to document-level simplification failing to coherently preserve the discourse structure of the document. We hypothesise that by providing a high-level view of the target document, a simplification plan might help to guide generation. Building upon previous work on controlled, sentence-level simplification, we view a plan as a sequence of labels, each describing one of four sentence-level simplification operations (copy, rephrase, split, or delete). We propose a planning model that labels each sentence in the input document while considering both its context (a window of surrounding sentences) and its internal structure (a token-level representation). Experiments on two simplification benchmarks (Newsela-auto and Wiki-auto) show that our model outperforms strong baselines both on the planning task and when used to guide document-level simplification models.
Models Teaching Models: Improving Model Accuracy with Slingshot Learning

One significant obstacle to the successful application of machine learning to real-world data is that of labeling: it is often prohibitively expensive to pay an ethical amount for the human labor required to label a dataset successfully. Human-in-the-loop techniques such as active learning can reduce the cost, but the required human time is still significant and many fixed costs remain. Another option is to employ pre-trained transformer models as labelers at scale, which can yield reasonable accuracy and significant cost savings. However, such models can still be expensive to use due to their high computational requirements, and the opaque nature of these models is not always suitable in applied social science and public use contexts. We propose a novel semi-supervised method, named Slingshot Learning, in which we iteratively and selectively augment a small human-labeled dataset with labels from a high-quality “teacher” model to slingshot the performance of a “student” model in a cost-efficient manner. This reduces the accuracy trade-off required to use these simpler algorithms without disrupting their benefits, such as lower compute requirements, better interpretability, and faster inference. We define and discuss the slingshot learning algorithm and demonstrate its effectiveness on several benchmark tasks, using ALBERT to teach a simple Naive Bayes binary classifier. We experimentally demonstrate that Slingshot learning effectively decreases the performance gap between the teacher and student models. We also analyze its performance in several scenarios and compare different variants of the algorithm.
Pento-DIARef: A Diagnostic Dataset for Learning the Incremental Algorithm for Referring Expression Generation from Examples

NLP tasks are typically defined extensionally through datasets containing example instantiations (e.g., pairs of image _i_ and text _t_), but motivated intensionally through capabilities invoked in verbal descriptions of the task (e.g., “_t_ is a description of _i_, for which the content of _i_ needs to be recognised and understood”).We present Pento-DIARef, a diagnostic dataset in a visual domain of puzzle pieces where referring expressions are generated by a well-known symbolic algorithm (the “Incremental Algorithm”),which itself is motivated by appeal to a hypothesised capability (eliminating distractors through application of Gricean maxims). Our question then is whether the extensional description (the dataset) is sufficient for a neural model to pick up the underlying regularity and exhibit this capability given the simple task definition of producing expressions from visual inputs. We find that a model supported by a vision detection step and a targeted data generation scheme achieves an almost perfect BLEU@1 score and sentence accuracy, whereas simpler baselines do not.
Paraphrase Acquisition from Image Captions

We propose to use image captions from the Web as a previously underutilized resource for paraphrases (i.e., texts with the same “message”) and to create and analyze a corresponding dataset. When an image is reused on the Web, an original caption is often assigned. We hypothesize that different captions for the same image naturally form a set of mutual paraphrases. To demonstrate the suitability of this idea, we analyze captions in the English Wikipedia, where editors frequently relabel the same image for different articles. The paper introduces the underlying mining technology, the resulting Wikipedia-IPC dataset, and compares known paraphrase corpora with respect to their syntactic and semantic paraphrase similarity to our new resource. In this context, we introduce characteristic maps along the two similarity dimensions to identify the style of paraphrases coming from different sources. An annotation study demonstrates the high reliability of the algorithmically determined characteristic maps.
Comparing Intrinsic Gender Bias Evaluation Measures without using Human Annotated Examples

Numerous types of social biases have been identified in pre-trained language models (PLMs), and various intrinsic bias evaluation measures have been proposed for quantifying those social biases.Prior works have relied on human annotated examples to compare existing intrinsic bias evaluation measures.However, this approach is not easily adaptable to different languages nor amenable to large scale evaluations due to the costs and difficulties when recruiting human annotators.To overcome this limitation, we propose a method to compare intrinsic gender bias evaluation measures without relying on human-annotated examples.Specifically, we create multiple bias-controlled versions of PLMs using varying amounts of male vs. female gendered sentences, mined automatically from an unannotated corpus using gender-related word lists. Next, each bias-controlled PLM is evaluated using an intrinsic bias evaluation measure, and the rank correlation between the computed bias scores and the gender proportions used to fine-tune the PLMs is computed.Experiments on multiple corpora and PLMs repeatedly show that the correlations reported by our proposed method that does not require human annotated examples are comparable to those computed using human annotated examples in prior work.
Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality

Dialogue summarization aims to condense a given dialogue into a simple and focused summary text. Typically, both the roles’ viewpoints and conversational topics change in the dialogue stream. Thus how to effectively handle the shifting topics and select the most salient utterance becomes one of the major challenges of this task. In this paper, we propose a novel topic-aware Global-Local Centrality (GLC) model to help select the salient context from all sub-topics. The centralities are constructed in both global level and local level. The global one aims to identify vital sub-topics in the dialogue and the local one aims to select the most important context in each sub-topic. Specifically, the GLC collects sub-topic based on the utterance representations. And each utterance is aligned with one sub-topic. Based on the sub-topics, the GLC calculates global- and local-level centralities. Finally, we combine the two to guide the model to capture both salient context and sub-topics when generating summaries. Experimental results show that our model outperforms strong baselines on three public dialogue summarization datasets: CSDS, MC, and SAMSUM. Further analysis demonstrates that our GLC can exactly identify vital contents from sub-topics.
kogito: A Commonsense Knowledge Inference Toolkit

In this paper, we present kogito, an open-source tool for generating commonsense inferences about situations described in text. kogito provides an intuitive and extensible interface to interact with natural language generation models that can be used for hypothesizing commonsense knowledge inference from a textual input. In particular, kogito offers several features for targeted, multi-granularity knowledge generation. These include a standardized API for training and evaluating knowledge models, and generating and filtering inferences from them. We also include helper functions for converting natural language texts into a format ingestible by knowledge models — intermediate pipeline stages such as knowledge head extraction from text, heuristic and model-based knowledge head-relation matching, and an ability to define and use custom knowledge relations. We make the code for kogito available at https://github.com/epfl-nlp/kogito along with thorough documentation at https://kogito.readthedocs.io.
A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System

Human-in-the-loop topic modelling incorporates users’ knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes, comparing different models and the lack of evaluation based on real-world examples of use. We developed a novel, interactive human-in-the-loop topic modeling system with a user-friendly interface that enables users compare and record every step they take, and a novel topic words suggestion feature to help users provide feedback that is faithful to the ground truth. Our system also supports not only what traditional topic models can do, i.e., learning the topics from the whole corpus, but also targeted topic modelling, i.e., learning topics for specific aspects of the corpus. In this article, we provide an overview of the system and present the results of a series of user studies designed to assess the value of the system in progressively more realistic applications of topic modelling.
CovRelex-SE: Adding Semantic Information for Relation Search via Sequence Embedding

Truong Do, Chau Nguyen, Vu Tran, Ken Satoh, Yuji Matsumoto, Minh Nguyen. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. 2023.
Analysis of Transfer Learning for Named Entity Recognition in South-Slavic Languages

This paper analyzes a Named Entity Recognition task for South-Slavic languages using the pre-trained multilingual neural network models. We investigate whether the performance of the models for a target language can be improved by using data from closely related languages. We have shown that the model performance is not influenced substantially when trained with other than a target language. While for Slovene, the monolingual setting generally performs better, for Croatian and Serbian the results are slightly better in selected cross-lingual settings, but the improvements are not large. The most significant performance improvement is shown for the Serbian language, which has the smallest corpora. Therefore, fine-tuning with other closely related languages may benefit only the “low resource” languages.
Croatian Film Review Dataset (Cro-FiReDa): A Sentiment Annotated Dataset of Film Reviews

This paper introduces Cro-FiReDa, a sentiment-annotated dataset for Croatian in the domain of movie reviews. The dataset, which contains over 10,000 sentences, has been annotated at the sentence level. In addition to presentingthe overall annotation process, we also present benchmark results based on the transformer-based fine-tuning approach.
Gender Differences in the Use of Emojis Among Genders

This study attempted to investigate the gender differences in the use of emojis on social media platforms. Furthermore, the differences in emojis preferences between men and women were investigated. The participants in this study included 10 of males and 10 of females. Data were collected through an adapted questionnaire from Persson (2019) consisting of 10 items. This study used a quantitative method; besides, the data were analyzed using descriptive statistical analyses. The results of this study revealed that there was no significant variation between men and women in the use of emojis. Moreover, the findings showed no significant variation among genders in their preferences of emojis.
SISTEM MONITORING KEAMANAN LINGKUNGAN PERUMAHAN MENGGUNAKAN RASPBERRY PI DAN MOTION EYE OS

Problems that often arise in residential areas are theft or housebreaking. the case above can be solved by using a surveillance camera, but rarely does anyone use it and the drawback of the existing surveillance cameras is that they cannot make direct notifications. In this study, the above problems can be solved by designing a security monitoring tool for the environment around housing based on webcam cameras and Raspberry Pi by utilizing Motioneye OS as a remote monitoring application, recording and capturing images and video results from motion detect and also notifications from pushover app. This study uses the SDLC method, namely the prototype model, in which this method is able to address real needs. The results in testing this study were that MotionEye OS captures and detects movement well and sends the detection results to pushovers in almost all conditions. for an alternative tool to replace the old system, this is quite good where this new system can monitor remotely and use any device and can receive state updates with notifications. With this security monitoring system tool, it is hoped that it can reduce criminal acts in residential areas.
Enhancing Information Retrieval in Fact Extraction and Verification

Modern fact verification systems have distanced themselves from the black box paradigm by providing the evidence used to infer their veracity judgments. Hence, evidence-backed fact verification systems’ performance heavily depends on the capabilities of their retrieval component to identify these facts. A popular evaluation benchmark for these systems is the FEVER task, which consists of determining the veracity of short claims using sentences extracted from Wikipedia. In this paper, we present a novel approach to the the retrieval steps of the FEVER task leveraging the graph structure of Wikipedia. The retrieval models surpass state of the art results at both sentence and document level. Additionally, we show that by feeding our retrieved evidence to the best-performing textual entailment model, we set a new state of the art in the FEVER competition.
Analyzing Zero-Shot transfer Scenarios across Spanish variants for Hate Speech Detection

Hate speech detection in online platforms has been widely studied inthe past. Most of these works were conducted in English and afew rich-resource languages. Recent approaches tailored forlow-resource languages have explored the interests of zero-shot cross-lingual transfer learning models in resource-scarce scenarios.However, languages variations between geolects such as AmericanEnglish and British English, Latin-American Spanish, and EuropeanSpanish is still a problem for NLP models that often relies on(latent) lexical information for their classification tasks. Moreimportantly, the cultural aspect, crucial for hate speech detection,is often overlooked.In this work, we present the results of a thorough analysis of hatespeech detection models performance on different variants of Spanish,including a new hate speech toward immigrants Twitter data set we built to cover these variants. Using mBERT and Beto, a monolingual Spanish Bert-based language model, as the basis of our transfer learning architecture, our results indicate that hate speech detection models for a given Spanish variant are affected when different variations of such language are not considered. Hate speech expressions could vary from region to region where the same language is spoken.Our new dataset, models and guidelines are freely available.
Generating Synthetic Speech from SpokenVocab for Speech Translation

Training end-to-end speech translation (ST) systems requires sufficiently large-scale data, which is unavailable for most language pairs and domains. One practical solution to the data scarcity issue is to convert text-based machine translation (MT) data to ST data via text-to-speech (TTS) systems.Yet, using TTS systems can be tedious and slow. In this work, we propose SpokenVocab, a simple, scalable and effective data augmentation technique to convert MT data to ST data on-the-fly. The idea is to retrieve and stitch audio snippets, corresponding to words in an MT sentence, from a spoken vocabulary bank. Our experiments on multiple language pairs show that stitched speech helps to improve translation quality by an average of 1.83 BLEU score, while performing equally well as TTS-generated speech in improving translation quality. We also showcase how SpokenVocab can be applied in code-switching ST for which often no TTS systems exit.