Miracle-dz/UniFAR-FTUs huggingface dataset:
New Intent Discovery with Pre-training and Contrastive Learning

New intent discovery aims to uncover novel intent categories from user utterances to expand the set of supported intent classes. It is a critical task for the development and service expansion of a practical dialogue system. Despite its importance, this problem remains under-explored in the literature. Existing approaches typically rely on a large amount of labeled utterances and employ pseudo-labeling methods for representation learning and clustering, which are label-intensive, inefficient, and inaccurate. In this paper, we provide new solutions to two important research questions for new intent discovery: (1) how to learn semantic utterance representations and (2) how to better cluster utterances. Particularly, we first propose a multi-task pre-training strategy to leverage rich unlabeled data along with external labeled data for representation learning. Then, we design a new contrastive loss to exploit self-supervisory signals in unlabeled data for clustering. Extensive experiments on three intent recognition benchmarks demonstrate the high effectiveness of our proposed method, which outperforms state-of-the-art methods by a large margin in both unsupervised and semi-supervised scenarios. The source code will be available at https://github.com/zhang-yu-wei/MTP-CLNN.
A comparison study on patient-psychologist voice diarization

Conversations between a clinician and a patient, in natural conditions, are valuable sources of information for medical follow-up. The automatic analysis of these dialogues could help extract new language markers and speed up the clinicians’ reports. Yet, it is not clear which model is the most efficient to detect and identify the speaker turns, especially for individuals with speech disorders. Here, we proposed a split of the data that allows conducting a comparative evaluation of different diarization methods. We designed and trained end-to-end neural network architectures to directly tackle this task from the raw signal and evaluate each approach under the same metric. We also studied the effect of fine-tuning models to find the best performance. Experimental results are reported on naturalistic clinical conversations between Psychologists and Interviewees, at different stages of Huntington’s disease, displaying a large panel of speech disorders. We found out that our best end-to-end model achieved 19.5 % IER on the test set, compared to 23.6% achieved by the finetuning of the X-vector architecture. Finally, we observed that we could extract clinical markers directly from the automatic systems, highlighting the clinical relevance of our methods.
On Controlling Fallback Responses for Grounded Dialogue Generation

Dialogue agents can leverage external textual knowledge to generate responses of a higher quality. To our best knowledge, most existing works on knowledge grounded dialogue settings assume that the user intention is always answerable. Unfortunately, this is impractical as there is no guarantee that the knowledge retrievers could always retrieve the desired knowledge. Therefore, this is crucial to incorporate fallback responses to respond to unanswerable contexts appropriately while responding to the answerable contexts in an informative manner. We propose a novel framework that automatically generates a control token with the generator to bias the succeeding response towards informativeness for answerable contexts and fallback for unanswerable contexts in an end-to-end manner. Since no existing knowledge grounded dialogue dataset considers this aim, we augment the existing dataset with unanswerable contexts to conduct our experiments. Automatic and human evaluation results indicate that naively incorporating fallback responses with controlled text generation still hurts informativeness for answerable context. In contrast, our proposed framework effectively mitigates this problem while still appropriately presenting fallback responses to unanswerable contexts. Such a framework also reduces the extra burden of the additional classifier and the overheads introduced in the previous works, which operates in a pipeline manner.
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages

Fine-grained entity typing (FGET) aims to classify named entity mentions into fine-grained entity types, which is meaningful for entity-related NLP tasks. For FGET, a key challenge is the low-resource problem — the complex entity type hierarchy makes it difficult to manually label data. Especially for those languages other than English, human-labeled data is extremely scarce. In this paper, we propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages. Specifically, we use multi-lingual pre-trained language models (PLMs) as the backbone to transfer the typing knowledge from high-resource languages (such as English) to low-resource languages (such as Chinese). Furthermore, we introduce entity-pair-oriented heuristic rules as well as machine translation to obtain cross-lingual distantly-supervised data, and apply cross-lingual contrastive learning on the distantly-supervised data to enhance the backbone PLMs. Experimental results show that by applying our framework, we can easily learn effective FGET models for low-resource languages, even without any language-specific human-labeled data. Our code is also available at https://github.com/thunlp/CrossET.
CogTaskonomy: Cognitively Inspired Task Taxonomy Is Beneficial to Transfer Learning in NLP

Is there a principle to guide transfer learning across tasks in natural language processing (NLP)? Taxonomy (Zamir et al., 2018) finds that a structure exists among visual tasks, as a principle underlying transfer learning for them. In this paper, we propose a cognitively inspired framework, CogTaskonomy, to learn taxonomy for NLP tasks. The framework consists of Cognitive Representation Analytics (CRA) and Cognitive-Neural Mapping (CNM). The former employs Representational Similarity Analysis, which is commonly used in computational neuroscience to find a correlation between brain-activity measurement and computational modeling, to estimate task similarity with task-specific sentence representations. The latter learns to detect task relations by projecting neural representations from NLP models to cognitive signals (i.e., fMRI voxels). Experiments on 12 NLP tasks, where BERT/TinyBERT are used as the underlying models for transfer learning, demonstrate that the proposed CogTaxonomy is able to guide transfer learning, achieving performance competitive to the Analytic Hierarchy Process (Saaty, 1987) used in visual Taskonomy (Zamir et al., 2018) but without requiring exhaustive pairwise O(m^2) task transferring. Analyses further discover that CNM is capable of learning model-agnostic task taxonomy.
DiBiMT: A Novel Benchmark for Measuring Word Sense Disambiguation Biases in Machine Translation

Lexical ambiguity poses one of the greatest challenges in the field of Machine Translation. Over the last few decades, multiple efforts have been undertaken to investigate incorrect translations caused by the polysemous nature of words. Within this body of research, some studies have posited that models pick up semantic biases existing in the training data, thus producing translation errors. In this paper, we present DiBiMT, the first entirely manually-curated evaluation benchmark which enables an extensive study of semantic biases in Machine Translation of nominal and verbal words in five different language combinations, namely, English and one or other of the following languages: Chinese, German, Italian, Russian and Spanish. Furthermore, we test state-of-the-art Machine Translation systems, both commercial and non-commercial ones, against our new test bed and provide a thorough statistical and linguistic analysis of the results. We release DiBiMT at https://nlp.uniroma1. it/dibimt as a closed benchmark with a public leaderboard.
Findings of the CONSTRAINT 2022 Shared Task on Detecting the Hero, the Villain, and the Victim in Memes

We present the findings of the shared task at the CONSTRAINT 2022 Workshop: Hero, Villain, and Victim: Dissecting harmful memes for Semantic role labeling of entities. The task aims to delve deeper into the domain of meme comprehension by deciphering the connotations behind the entities present in a meme. In more nuanced terms, the shared task focuses on determining the victimizing, glorifying, and vilifying intentions embedded in meme entities to explicate their connotations. To this end, we curate HVVMemes, a novel meme dataset of about 7000 memes spanning the domains of COVID-19 and US Politics, each containing entities and their associated roles: hero, villain, victim, or none. The shared task attracted 105 participants, but eventually only 6 submissions were made. Most of the successful submissions relied on fine-tuning pre-trained language and multimodal models along with ensembles. The best submission achieved an F1-score of 58.67.
Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze?

Learned self-attention functions in state-of-the-art NLP models often correlate with human attention. We investigate whether self-attention in large-scale pre-trained language models is as predictive of human eye fixation patterns during task-reading as classical cognitive models of human attention. We compare attention functions across two task-specific reading datasets for sentiment analysis and relation extraction. We find the predictiveness of large-scale pre-trained self-attention for human attention depends on ‘what is in the tail’, e.g., the syntactic nature of rare contexts.Further, we observe that task-specific fine-tuning does not increase the correlation with human task-specific reading. Through an input reduction experiment we give complementary insights on the sparsity and fidelity trade-off, showing that lower-entropy attention vectors are more faithful.
Modeling Multi-hop Question Answering as Single Sequence Prediction

Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA. However, the complexity of multi-hop QA hinders the effectiveness of the generative QA approach. In this work, we propose a simple generative approach (PathFid) that extends the task beyond just answer generation by explicitly modeling the reasoning process to resolve the answer for multi-hop questions. By linearizing the hierarchical reasoning path of supporting passages, their key sentences, and finally the factoid answer, we cast the problem as a single sequence prediction task. To facilitate complex reasoning with multiple clues, we further extend the unified flat representation of multiple input documents by encoding cross-passage interactions. Our extensive experiments demonstrate that PathFid leads to strong performance gains on two multi-hop QA datasets: HotpotQA and IIRC. Besides the performance gains, PathFid is more interpretable, which in turn yields answers that are more faithfully grounded to the supporting passages and facts compared to the baseline Fid model.
Modeling Syntactic-Semantic Dependency Correlations in Semantic Role Labeling Using Mixture Models

In this paper, we propose a mixture model-based end-to-end method to model the syntactic-semantic dependency correlation in Semantic Role Labeling (SRL). Semantic dependencies in SRL are modeled as a distribution over semantic dependency labels conditioned on a predicate and an argument word.The semantic label distribution varies depending on Shortest Syntactic Dependency Path (SSDP) hop patterns.We target the variation of semantic label distributions using a mixture model, separately estimating semantic label distributions for different hop patterns and probabilistically clustering hop patterns with similar semantic label distributions.Experiments show that the proposed method successfully learns a cluster assignment reflecting the variation of semantic label distributions.Modeling the variation improves performance in predicting short distance semantic dependencies, in addition to the improvement on long distance semantic dependencies that previous syntax-aware methods have achieved.The proposed method achieves a small but statistically significant improvement over baseline methods in English, German, and Spanish and obtains competitive performance with state-of-the-art methods in English.
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph

Chinese pre-trained language models usually exploit contextual character information to learn representations, while ignoring the linguistics knowledge, e.g., word and sentence information. Hence, we propose a task-free enhancement module termed as Heterogeneous Linguistics Graph (HLG) to enhance Chinese pre-trained language models by integrating linguistics knowledge. Specifically, we construct a hierarchical heterogeneous graph to model the characteristics linguistics structure of Chinese language, and conduct a graph-based method to summarize and concretize information on different granularities of Chinese linguistics hierarchies.Experimental results demonstrate our model has the ability to improve the performance of vanilla BERT, BERTwwm and ERNIE 1.0 on 6 natural language processing tasks with 10 benchmark datasets. Further, the detailed experimental analyses have proven that this kind of modelization achieves more improvements compared with previous strong baseline MWA. Meanwhile, our model introduces far fewer parameters (about half of MWA) and the training/inference speed is about 7x faster than MWA.
Entity-based Neural Local Coherence Modeling

In this paper, we propose an entity-based neural local coherence model which is linguistically more sound than previously proposed neural coherence models. Recent neural coherence models encode the input document using large-scale pretrained language models. Hence their basis for computing local coherence are words and even sub-words. The analysis of their output shows that these models frequently compute coherence on the basis of connections between (sub-)words which, from a linguistic perspective, should not play a role. Still, these models achieve state-of-the-art performance in several end applications. In contrast to these models, we compute coherence on the basis of entities by constraining the input to noun phrases and proper names. This provides us with an explicit representation of the most important items in sentences leading to the notion of focus. This brings our model linguistically in line with pre-neural models of computing coherence. It also gives us better insight into the behaviour of the model thus leading to better explainability. Our approach is also in accord with a recent study (O’Connor and Andreas, 2021), which shows that most usable information is captured by nouns and verbs in transformer-based language models. We evaluate our model on three downstream tasks showing that it is not only linguistically more sound than previous models but also that it outperforms them in end applications.
MTL-SLT: Multi-Task Learning for Spoken Language Tasks

Language understanding in speech-based systems has attracted extensive interest from both academic and industrial communities in recent years with the growing demand for voice-based applications. Prior works focus on independent research by the automatic speech recognition (ASR) and natural language processing (NLP) communities, or on jointly modeling the speech and NLP problems focusing on a single dataset or single NLP task. To facilitate the development of spoken language research, we introduce MTL-SLT, a multi-task learning framework for spoken language tasks. MTL-SLT takes speech as input, and outputs transcription, intent, named entities, summaries, and answers to text queries, supporting the tasks of spoken language understanding, spoken summarization and spoken question answering respectively. The proposed framework benefits from three key aspects: 1) pre-trained sub-networks of ASR model and language model; 2) multi-task learning objective to exploit shared knowledge from different tasks; 3) end-to-end training of ASR and downstream NLP task based on sequence loss. We obtain state-of-the-art results on spoken language understanding tasks such as SLURP and ATIS. Spoken summarization results are reported on a new dataset: Spoken-Gigaword.
RST Discourse Parsing with Second-Stage EDU-Level Pre-training

Pre-trained language models (PLMs) have shown great potentials in natural language processing (NLP) including rhetorical structure theory (RST) discourse parsing.Current PLMs are obtained by sentence-level pre-training, which is different from the basic processing unit, i.e. element discourse unit (EDU). To this end, we propose a second-stage EDU-level pre-training approach in this work, which presents two novel tasks to learn effective EDU representations continually based on well pre-trained language models.Concretely, the two tasks are (1) next EDU prediction (NEP) and (2) discourse marker prediction (DMP). We take a state-of-the-art transition-based neural parser as baseline, and adopt it with a light bi-gram EDU modification to effectively explore the EDU-level pre-trained EDU representation.Experimental results on a benckmark dataset show that our method is highly effective,leading a 2.1-point improvement in F1-score.All codes and pre-trained models will be released publicly to facilitate future studies.
Evaluating the Impact of 1:1 Laptops on High School Science Students and Teachers

This thesis is the culmination of a 6-year-long longitudinal study into the impact of 1:1 laptops on the experiences and achievements of high school science teachers and students. Set in the context of 16 Sydney high schools during the Australian Digital Education Revolution, this thesis explores the practices of teachers and students with 1:1 laptops in the sciences, the impact of the 1:1 laptops on student attainment in standardised external examinations, and ultimately investigates the reasons behind the findings. As a thesis-by-publication, this thesis consists of two introductory chapters, five journal papers (four of which have been published in peer-reviewed journals, with the fifth under review) making up five chapters, an overall discussion and a self-reflection. Ultimately, this thesis provides a detailed, mixed methods commentary of the experiences of schools, teachers and students over the five years of the much maligned Digital Education Revolution, something that is missing in the national public domain. Within the larger sphere of educational technology research globally, this thesis contributes to filling in some of the gaps existing in the extant literature, particularly in terms of quantitative analysis and statistically significant findings. Future research would benefit from the methodologies, visual representations and overall findings contained within this thesis. In fact, several recent eminent literature reviews and meta-analyses include some of the papers that make up this thesis.
Utility Preservation of Clinical Text After De-Identification

Electronic health records contain valuable information about symptoms, diagnosis, treatment and outcomes of the treatments of individual patients. However, the records may also contain information that can reveal the identity of the patients. Removing these identifiers - the Protected Health Information (PHI) - can protect the identity of the patient. Automatic de-identification is a process which employs machine learning techniques to detect and remove PHI. However, automatic techniques are imperfect in their precision and introduce noise into the data. This study examines the impact of this noise on the utility of Swedish de-identified clinical data by using human evaluators and by training and testing BERT models. Our results indicate that de-identification does not harm the utility for clinical NLP and that human evaluators are less sensitive to noise from de-identification than expected.
The Best of both Worlds: Dual Channel Language modeling for Hope Speech Detection in low-resourced Kannada

In recent years, various methods have been developed to control the spread of negativity by removing profane, aggressive, and offensive comments from social media platforms. There is, however, a scarcity of research focusing on embracing positivity and reinforcing supportive and reassuring content in online forums. As a result, we concentrate our research on developing systems to detect hope speech in code-mixed Kannada. As a result, we present DC-LM, a dual-channel language model that sees hope speech by using the English translations of the code-mixed dataset for additional training. The approach is jointly modelled on both English and code-mixed Kannada to enable effective cross-lingual transfer between the languages. With a weighted F1-score of 0.756, the method outperforms other models. We aim to initiate research in Kannada while encouraging researchers to take a pragmatic approach to inspire positive and supportive online content.
Pretrained Speech Encoders and Efficient Fine-tuning Methods for Speech Translation: UPC at IWSLT 2022

This paper describes the submissions of the UPC Machine Translation group to the IWSLT 2022 Offline Speech Translation and Speech-to-Speech Translation tracks. The offline task involves translating English speech to German, Japanese and Chinese text. Our Speech Translation systems are trained end-to-end and are based on large pretrained speech and text models. We use an efficient fine-tuning technique that trains only specific layers of our system, and explore the use of adapter modules for the non-trainable layers. We further investigate the suitability of different speech encoders (wav2vec 2.0, HuBERT) for our models and the impact of knowledge distillation from the Machine Translation model that we use for the decoder (mBART). For segmenting the IWSLT test sets we fine-tune a pretrained audio segmentation model and achieve improvements of 5 BLEU compared to the given segmentation. Our best single model uses HuBERT and parallel adapters and achieves 29.42 BLEU at English-German MuST-C tst-COMMON and 26.77 at IWSLT 2020 test. By ensembling many models, we further increase translation quality to 30.83 BLEU and 27.78 accordingly. Furthermore, our submission for English-Japanese achieves 15.85 and English-Chinese obtains 25.63 BLEU on the MuST-C tst-COMMON sets. Finally, we extend our system to perform English-German Speech-to-Speech Translation with a pretrained Text-to-Speech model.
Revisiting Automatic Evaluation of Extractive Summarization Task: Can We Do Better than ROUGE?

It has been the norm for a long time to evaluate automated summarization tasks using the popular ROUGE metric. Although several studies in the past have highlighted the limitations of ROUGE, researchers have struggled to reach a consensus on a better alternative until today. One major limitation of the traditional ROUGE metric is the lack of semantic understanding (relies on direct overlap of n-grams). In this paper, we exclusively focus on the extractive summarization task and propose a semantic-aware nCG (normalized cumulative gain)-based evaluation metric (called Sem-nCG) for evaluating this task. One fundamental contribution of the paper is that it demonstrates how we can generate more reliable semantic-aware ground truths for evaluating extractive summarization tasks without any additional human intervention. To the best of our knowledge, this work is the first of its kind. We have conducted extensive experiments with this new metric using the widely used CNN/DailyMail dataset. Experimental results show that the new Sem-nCG metric is indeed semantic-aware, shows higher correlation with human judgement (more reliable) and yields a large number of disagreements with the original ROUGE metric (suggesting that ROUGE often leads to inaccurate conclusions also verified by humans).