Miracle-dz/UniFAR-FTUs huggingface dataset:
A Time-Aware Transformer Based Model for Suicide Ideation Detection on Social Media

Social media’s ubiquity fosters a space for users to exhibit suicidal thoughts outside of traditional clinical settings. Understanding the build-up of such ideation is critical for the identification of at-risk users and suicide prevention. Suicide ideation is often linked to a history of mental depression. The emotional spectrum of a user’s historical activity on social media can be indicative of their mental state over time. In this work, we focus on identifying suicidal intent in English tweets by augmenting linguistic models with historical context. We propose STATENet, a time-aware transformer based model for preliminary screening of suicidal risk on social media. STATENet outperforms competitive methods, demonstrating the utility of emotional and temporal contextual cues for suicide risk assessment. We discuss the empirical, qualitative, practical, and ethical aspects of STATENet for suicide ideation detection.
Querying Across Genres for Medical Claims in News

We present a query-based biomedical information retrieval task across two vastly different genres – newswire and research literature – where the goal is to find the research publication that supports the primary claim made in a health-related news article. For this task, we present a new dataset of 5,034 claims from news paired with research abstracts. Our approach consists of two steps: (i) selecting the most relevant candidates from a collection of 222k research abstracts, and (ii) re-ranking this list. We compare the classical IR approach using BM25 with more recent transformer-based models. Our results show that cross-genre medical IR is a viable task, but incorporating domain-specific knowledge is crucial.
T3: Tree-Autoencoder Regularized Adversarial Text Generation for Targeted Attack

Adversarial attacks against natural language processing systems, which perform seemingly innocuous modiﬁcations to inputs, can induce arbitrary mistakes to the target models. Though raised great concerns, such adversarial attacks can be leveraged to estimate the robustness of NLP models. Compared with the adversarial example generation in continuous data domain ( e.g. , image), generating adversarial text that preserves the original meaning is challenging since the text space is discrete and non-differentiable. To handle these challenges, we propose a target-controllable adversarial attack framework T3, which is applicable to a range of NLP tasks. In particular, we propose a tree-based autoencoder to embed the discrete text data into a continuous representation space, upon which we optimize the adversarial perturbation. A novel tree-based de-coder is then applied to regularize the syntactic correctness of the generated text and manipulate it on either sentence (T3(S ENT )) or word (T3(W ORD )) level. We consider two most representative NLP tasks: sentiment analysis and question answering (QA). Extensive experimental results and human studies show that T3 generated adversarial texts can successfully manipulate the NLP models to output the targeted incorrect answer without misleading the human. Moreover, we show that the generated adversarial texts have high transfer-ability which enables the black-box
Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors

Most recent improvements in NLP come from changes to the neural network architectures modeling the text input. Yet, state-of-the-art models often rely on simple approaches to model the label space, e.g. bigram Conditional Random Fields (CRFs) in sequence tagging. More expressive graphical models are rarely used due to their prohibitive computational cost. In this work, we present an approach for efficiently training and decoding hybrids of graphical models and neural networks based on Gibbs sampling. Our approach is the natural adaptation of SampleRank (Wick et al., 2011) to neural models, and is widely applicable to tasks beyond sequence tagging. We apply our approach to named entity recognition and present a neural skip-chain CRF model, for which exact inference is impractical. The skip-chain model improves over a strong baseline on three languages from CoNLL-02/03. We obtain new state-of-the-art results on Dutch.
Explainable Clinical Decision Support from Text

Clinical prediction models often use structured variables and provide outcomes that are not readily interpretable by clinicians. Further, free-text medical notes may contain information not immediately available in structured variables. We propose a hierarchical CNN-transformer model with explicit attention as an interpretable, multi-task clinical language model, which achieves an AUROC of 0.75 and 0.78 on sepsis and mortality prediction, respectively. We also explore the relationships between learned features from structured and unstructured variables using projection-weighted canonical correlation analysis. Finally, we outline a protocol to evaluate model usability in a clinical decision support context. From domain-expert evaluations, our model generates informative rationales that have promising real-life applications.
Scaling Hidden Markov Language Models

The hidden Markov model (HMM) is a fundamental tool for sequence modeling that cleanly separates the hidden state from the emission structure. However, this separation makes it difficult to fit HMMs to large datasets in modern NLP, and they have fallen out of use due to very poor performance compared to fully observed models. This work revisits the challenge of scaling HMMs to language modeling datasets, taking ideas from recent approaches to neural modeling. We propose methods for scaling HMMs to massive state spaces while maintaining efficient exact inference, a compact parameterization, and effective regularization. Experiments show that this approach leads to models that are more accurate than previous HMM and n-gram-based methods, making progress towards the performance of state-of-the-art neural models.
Counterfactual Generator: A Weakly-Supervised Method for Named Entity Recognition

Past progress on neural models has proven that named entity recognition is no longer a problem if we have enough labeled data. However, collecting enough data and annotating them are labor-intensive, time-consuming, and expensive. In this paper, we decompose the sentence into two parts: entity and context, and rethink the relationship between them and model performance from a causal perspective. Based on this, we propose the Counterfactual Generator, which generates counterfactual examples by the interventions on the existing observational examples to enhance the original dataset. Experiments across three datasets show that our method improves the generalization ability of models under limited observational examples. Besides, we provide a theoretical foundation by using a structural causal model to explore the spurious correlations between input features and output labels. We investigate the causal effects of entity or context on model performance under both conditions: the non-augmented and the augmented. Interestingly, we find that the non-spurious correlations are more located in entity representation rather than context representation. As a result, our method eliminates part of the spurious correlations between context representation and output labels. The code is available at https://github.com/xijiz/cfgen.
Bridging the Gap between Prior and Posterior Knowledge Selection for Knowledge-Grounded Dialogue Generation

Knowledge selection plays an important role in knowledge-grounded dialogue, which is a challenging task to generate more informative responses by leveraging external knowledge. Recently, latent variable models have been proposed to deal with the diversity of knowledge selection by using both prior and posterior distributions over knowledge and achieve promising performance. However, these models suffer from a huge gap between prior and posterior knowledge selection. Firstly, the prior selection module may not learn to select knowledge properly because of lacking the necessary posterior information. Secondly, latent variable models suffer from the exposure bias that dialogue generation is based on the knowledge selected from the posterior distribution at training but from the prior distribution at inference. Here, we deal with these issues on two aspects: (1) We enhance the prior selection module with the necessary posterior information obtained from the specially designed Posterior Information Prediction Module (PIPM); (2) We propose a Knowledge Distillation Based Training Strategy (KDBTS) to train the decoder with the knowledge selected from the prior distribution, removing the exposure bias of knowledge selection. Experimental results on two knowledge-grounded dialogue datasets show that both PIPM and KDBTS achieve performance improvement over the state-of-the-art latent variable model and their combination shows further improvement.
Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder

Topic models have been prevailing for many years on discovering latent semantics while modeling long documents. However, for short texts they generally suffer from data sparsity because of extremely limited word co-occurrences; thus tend to yield repetitive or trivial topics with low quality. In this paper, to address this issue, we propose a novel neural topic model in the framework of autoencoding with a new topic distribution quantization approach generating peakier distributions that are more appropriate for modeling short texts. Besides the encoding, to tackle this issue in terms of decoding, we further propose a novel negative sampling decoder learning from negative samples to avoid yielding repetitive topics. We observe that our model can highly improve short text topic modeling performance. Through extensive experiments on real-world datasets, we demonstrate our model can outperform both strong traditional and neural baselines under extreme data sparsity scenes, producing high-quality topics.
MODE-LSTM: A Parameter-efficient Recurrent Network with Multi-Scale for Sentence Classification

The central problem of sentence classification is to extract multi-scale n-gram features for understanding the semantic meaning of sentences. Most existing models tackle this problem by stacking CNN and RNN models, which easily leads to feature redundancy and overfitting because of relatively limited datasets. In this paper, we propose a simple yet effective model called Multi-scale Orthogonal inDependEnt LSTM (MODE-LSTM), which not only has effective parameters and good generalization ability, but also considers multiscale n-gram features. We disentangle the hidden state of the LSTM into several independently updated small hidden states and apply an orthogonal constraint on their recurrent matrices. We then equip this structure with sliding windows of different sizes for extracting multi-scale n-gram features. Extensive experiments demonstrate that our model achieves better or competitive performance against state-of-the-art baselines on eight benchmark datasets. We also combine our model with BERT to further boost the generalization performance.
Learn to Cross-lingual Transfer with Meta Graph Learning across Heterogeneous Languages

Recent emergence of multilingual pre-training language model (mPLM) has enabled breakthroughs on various downstream cross-lingual transfer (CLT) tasks. However, mPLM-based methods usually involve two problems: (1) simply fine-tuning may not adapt general-purpose multilingual representations to be task-aware on low-resource languages; (2) ignore how cross-lingual adaptation happens for downstream tasks. To address the issues, we propose a meta graph learning (MGL) method. Unlike prior works that transfer from scratch, MGL can learn to cross-lingual transfer by extracting meta-knowledge from historical CLT experiences (tasks), making mPLM insensitive to low-resource languages. Besides, for each CLT task, MGL formulates its transfer process as information propagation over a dynamic graph, where the geometric structure can automatically capture intrinsic language relationships to explicitly guide cross-lingual transfer. Empirically, extensive experiments on both public and real-world datasets demonstrate the effectiveness of the MGL method.
Introducing a New Dataset for Event Detection in Cybersecurity Texts

Detecting cybersecurity events is necessary to keep us informed about the fast growing number of such events reported in text. In this work, we focus on the task of event detection (ED) to identify event trigger words for the cybersecurity domain. In particular, to facilitate the future research, we introduce a new dataset for this problem, characterizing the manual annotation for 30 important cybersecurity event types and a large dataset size to develop deep learning models. Comparing to the prior datasets for this task, our dataset involves more event types and supports the modeling of document-level information to improve the performance. We perform extensive evaluation with the current state-of-the-art methods for ED on the proposed dataset. Our experiments reveal the challenges of cybersecurity ED and present many research opportunities in this area for the future work.
Detecting Independent Pronoun Bias with Partially-Synthetic Data Generation

We report that state-of-the-art parsers consistently failed to identify “hers” and “theirs” as pronouns but identified the masculine equivalent “his”. We find that the same biases exist in recent language models like BERT. While some of the bias comes from known sources, like training data with gender imbalances, we find that the bias is _amplified_ in the language models and that linguistic differences between English pronouns that are not inherently biased can become biases in some machine learning models. We introduce a new technique for measuring bias in models, using Bayesian approximations to generate partially-synthetic data from the model itself.
Template Guided Text Generation for Task Oriented Dialogue

Virtual assistants such as Google Assistant, Amazon Alexa, and Apple Siri enable users to interact with a large number of services and APIs on the web using natural language. In this work, we investigate two methods for Natural Language Generation (NLG) using a single domain-independent model across a large number of APIs. First, we propose a schema-guided approach which conditions the generation on a schema describing the API in natural language. Our second method investigates the use of a small number of templates, growing linearly in number of slots, to convey the semantics of the API. To generate utterances for an arbitrary slot combination, a few simple templates are first concatenated to give a semantically correct, but possibly incoherent and ungrammatical utterance. A pre-trained language model is subsequently employed to rewrite it into coherent, natural sounding text. Through automatic metrics and human evaluation, we show that our method improves over strong baselines, is robust to out-of-domain inputs and shows improved sample efficiency.
Slot Attention with Value Normalization for Multi-domain Dialogue State Tracking

Incompleteness of domain ontology and unavailability of some values are two inevitable problems of dialogue state tracking (DST). Existing approaches generally fall into two extremes: choosing models without ontology or embedding ontology in models leading to over-dependence. In this paper, we propose a new architecture to cleverly exploit ontology, which consists of Slot Attention (SA) and Value Normalization (VN), referred to as SAVN. Moreover, we supplement the annotation of supporting span for MultiWOZ 2.1, which is the shortest span in utterances to support the labeled value. SA shares knowledge between slots and utterances and only needs a simple structure to predict the supporting span. VN is designed specifically for the use of ontology, which can convert supporting spans to the values. Empirical results demonstrate that SAVN achieves the state-of-the-art joint accuracy of 54.52% on MultiWOZ 2.0 and 54.86% on MultiWOZ 2.1. Besides, we evaluate VN with incomplete ontology. The results show that even if only 30% ontology is used, VN can also contribute to our model.
IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation

Context-dependent text-to-SQL task has drawn much attention in recent years. Previous models on context-dependent text-to-SQL task only concentrate on utilizing historical user inputs. In this work, in addition to using encoders to capture historical information of user inputs, we propose a database schema interaction graph encoder to utilize historicalal information of database schema items. In decoding phase, we introduce a gate mechanism to weigh the importance of different vocabularies and then make the prediction of SQL tokens. We evaluate our model on the benchmark SParC and CoSQL datasets, which are two large complex context-dependent cross-domain text-to-SQL datasets. Our model outperforms previous state-of-the-art model by a large margin and achieves new state-of-the-art results on the two datasets. The comparison and ablation results demonstrate the efficacy of our model and the usefulness of the database schema interaction graph encoder.
Inducing Target-specific Latent Structures for Aspect Sentiment Classification

Aspect-level sentiment analysis aims to recognize the sentiment polarity of an aspect or a target in a comment. Recently, graph convolutional networks based on linguistic dependency trees have been studied for this task. However, the dependency parsing accuracy of commercial product comments or tweets might be unsatisfactory. To tackle this problem, we associate linguistic dependency trees with automatically induced aspectspecific graphs. We propose gating mechanisms to dynamically combine information from word dependency graphs and latent graphs which are learned by self-attention networks. Our model can complement supervised syntactic features with latent semantic dependencies. Experimental results on five benchmarks show the effectiveness of our proposed latent models, giving significantly better results than models without using latent graphs.
Zero-Shot Crosslingual Sentence Simplification

Sentence simplification aims to make sentences easier to read and understand. Recent approaches have shown promising results with encoder-decoder models trained on large amounts of parallel data which often only exists in English. We propose a zero-shot modeling framework which transfers simplification knowledge from English to another language (for which no parallel simplification corpus exists) while generalizing across languages and tasks. A shared transformer encoder constructs language-agnostic representations, with a combination of task-specific encoder layers added on top (e.g., for translation and simplification). Empirical results using both human and automatic metrics show that our approach produces better simplifications than unsupervised and pivot-based methods.
The Thieves on Sesame Street Are Polyglots — Extracting Multilingual Models from Monolingual APIs

Pre-training in natural language processing makes it easier for an adversary with only query access to a victim model to reconstruct a local copy of the victim by training with gibberish input data paired with the victim’s labels for that data. We discover that this extraction process extends to local copies initialized from a pre-trained, multilingual model while the victim remains monolingual. The extracted model learns the task from the monolingual victim, but it generalizes far better than the victim to several other languages. This is done without ever showing the multilingual, extracted model a well-formed input in any of the languages for the target task. We also demonstrate that a few real examples can greatly improve performance, and we analyze how these results shed light on how such extraction methods succeed.
Relation-aware Graph Attention Networks with Relational Position Encodings for Emotion Recognition in Conversations

Interest in emotion recognition in conversations (ERC) has been increasing in various fields, because it can be used to analyze user behaviors and detect fake news. Many recent ERC methods use graph-based neural networks to take the relationships between the utterances of the speakers into account. In particular, the state-of-the-art method considers self- and inter-speaker dependencies in conversations by using relational graph attention networks (RGAT). However, graph-based neural networks do not take sequential information into account. In this paper, we propose relational position encodings that provide RGAT with sequential information reflecting the relational graph structure. Accordingly, our RGAT model can capture both the speaker dependency and the sequential information. Experiments on four ERC datasets show that our model is beneficial to recognizing emotions expressed in conversations. In addition, our approach empirically outperforms the state-of-the-art on all of the benchmark datasets.
Alignment-free Cross-lingual Semantic Role Labeling

Cross-lingual semantic role labeling (SRL) aims at leveraging resources in a source language to minimize the effort required to construct annotations or models for a new target language. Recent approaches rely on word alignments, machine translation engines, or preprocessing tools such as parsers or taggers. We propose a cross-lingual SRL model which only requires annotations in a source language and access to raw text in the form of a parallel corpus. The backbone of our model is an LSTM-based semantic role labeler jointly trained with a semantic role compressor and multilingual word embeddings. The compressor collects useful information from the output of the semantic role labeler, filtering noisy and conflicting evidence. It lives in a multilingual embedding space and provides direct supervision for predicting semantic roles in the target language. Results on the Universal Proposition Bank and manually annotated datasets show that our method is highly effective, even against systems utilizing supervised features.
Improving Word Sense Disambiguation with Translations

It has been conjectured that multilingual information can help monolingual word sense disambiguation (WSD). However, existing WSD systems rarely consider multilingual information, and no effective method has been proposed for improving WSD by generating translations. In this paper, we present a novel approach that improves the performance of a base WSD system using machine translation. Since our approach is language independent, we perform WSD experiments on several languages. The results demonstrate that our methods can consistently improve the performance of WSD systems, and obtain state-ofthe-art results in both English and multilingual WSD. To facilitate the use of lexical translation information, we also propose BABALIGN, an precise bitext alignment algorithm which is guided by multilingual lexical correspondences from BabelNet.
Annotating Temporal Dependency Graphs via Crowdsourcing

We present the construction of a corpus of 500 Wikinews articles annotated with temporal dependency graphs (TDGs) that can be used to train systems to understand temporal relations in text. We argue that temporal dependency graphs, built on previous research on narrative times and temporal anaphora, provide a representation scheme that achieves a good trade-off between completeness and practicality in temporal annotation. We also provide a crowdsourcing strategy to annotate TDGs, and demonstrate the feasibility of this approach with an evaluation of the quality of the annotation, and the utility of the resulting data set by training a machine learning model on this data set. The data set is publicly available.