Classification Task

A machine translated sentence is seldom completely correct. Confidence measures are designed to detect incorrect words, phrases or sentences, or to provide an esti- mation of the probability of correctness. …...

Weblogs are a source of human activity know- ledge comprising valuable information such as facts, opinions and personal experiences. In this paper, we propose a method for mining personal experiences …...

Native language identification (NLI) is the task of determining the native language of an author writing in a second language. Several pieces of earlier work have found that fea- tures …...

Evidence-based medicine is an approach whereby clinical decisions are supported by the best available findings gained from scien- tific research. This requires efficient access to such evidence. To this end, …...

When people describe a scene, they often in- clude information that is not visually apparent; sometimes based on background knowledge, sometimes to tell a story. We aim to sepa- rate …...

AbstractAlthough the current transcription systems could achieve high recognition performance, they still have a lot of difficulties to transcribe speech in very noisy environments. The transcription quality has a direct …...

This paper presents our approach to se- mantic relatedness and textual entailment subtasks organized as task 1 in SemEval 2014. Specifically, we address two ques- tions: (1) Can we solve …...

Unlike traditional over-the-phone spoken dialog systems (SDSs), modern dialog systems tend to have visual rendering on the device screen as an additional modal- ity to communicate the system’s response to …...

Hyper-parameter optimization is an im- portant problem in natural language pro- cessing (NLP) and machine learning. Re- cently, a group of studies has focused on using sequential Bayesian Optimization to …...

Users prefer natural language software re- quirements because of their usability and ac- cessibility. When they describe their wishes for software development, they often provide off-topic information. We therefore present …...

AbstractWe describe our system for SemEval-2018 Shared Task on Semantic Relation Extraction and Classification in Scientific Papers where we focus on the Classification task. Our simple piecewise convolution neural encoder …...

AbstractThis paper reports on a reimplementation of a system on detecting implicit positive meaning from negated statements. In the original regression experiment, different positive interpretations per negation are scored according …...

AbstractNatural language has recently been explored as a new medium of supervision for training machine learning models. Here, we explore learning classification tasks using language in a conversational setting – …...

AbstractNatural Language Processing (NLP) can help unlock the vast troves of unstructured data in clinical text and thus improve healthcare research. However, a big barrier to developments in this field …...

AbstractDocument structure extraction has been a widely researched area for decades with recent works performing it as a semantic segmentation task over document images using fully-convolution networks. Such methods are …...

AbstractThis position paper describes and critiques the Pretraining-Agnostic Identically Distributed (PAID) evaluation paradigm, which has become a central tool for measuring progress in natural language understanding. This paradigm consists of …...

AbstractWe introduce a new classification task for scientific statements and release a large-scale dataset for supervised learning. Our resource is derived from a machine-readable representation of the arXiv.org collection of …...

AbstractLarge transformer-based language models have been shown to be very effective in many classification tasks. However, their computational complexity prevents their use in applications requiring the classification of a large …...

AbstractDefeasible inference is a mode of reasoning in which an inference (X is a bird, therefore X flies) may be weakened or overturned in light of new evidence (X is …...

AbstractFor many prediction tasks, stakeholders desire not only predictions but also supporting evidence that a human can use to verify its correctness. However, in practice, evidence annotations may only be …...

AbstractMuch of the progress in contemporary NLP has come from learning representations, such as masked language model (MLM) contextual embeddings, that turn challenging problems into simple classification tasks. But how …...

AbstractA health outcome is a measurement or an observation used to capture and assess the effect of a treatment. Automatic detection of health outcomes from text would undoubtedly speed up …...

AbstractWhile pre-trained language models (PLMs) are the go-to solution to tackle many natural language processing problems, they are still very limited in their ability to capture and to use common-sense …...

AbstractSelective rationalization aims to produce decisions along with rationales (e.g., text highlights or word alignments between two sentences). Commonly, rationales are modeled as stochastic binary masks, requiring sampling-based gradient estimators, …...

Time-to-event prediction tasks are common in conversation modelling, for applications such as predicting the length of a conversation or when a user will stop contributing to a plat- form. Despite …...

AbstractIntermediate task fine-tuning has been shown to culminate in large transfer gains across many NLP tasks. With an abundance of candidate datasets as well as pre-trained language models, it has …...

AbstractThe use of contrastive loss for representation learning has become prominent in computer vision, and it is now getting attention in Natural Language Processing (NLP).Here, we explore the idea of …...

AbstractIn this paper, we compare the performance of two BERT-based text classifiers whose task is to classify patients (more precisely, their medical histories) as having or not having implant(s) in …...

AbstractSupervised learning has traditionally focused on inductive learning by observing labeled examples of a task. In contrast, a hallmark of human intelligence is the ability to learn new concepts purely …...

AbstractHate speech is plaguing the cyberspace along with user-generated content. Adding counter speech has become an effective way to combat hate speech online. Existing datasets and models target either (a) …...

AbstractLarge language models (LMs) are able to in-context learn—perform a new task via inference alone by conditioning on a few input-label pairs (demonstrations) and making predictions for new inputs. However, …...

AbstractPrompting has shown impressive success in enabling large pre-trained language models (LMs) to perform diverse NLP tasks, especially with only few downstream data. Automatically finding the optimal prompt for each …...

AbstractA few large, homogenous, pre-trained models undergird many machine learning systems — and often, these models contain harmful stereotypes learned from the internet. We investigate the bias transfer hypothesis: the …...

AbstractProviding natural language instructions in prompts is a useful new paradigm for improving task performance of large language models in a zero-shot setting. Recent work has aimed to improve such …...

AbstractLarge pre-trained language models contain societal biases and carry along these biases to downstream tasks. Current in-processing bias mitigation approaches (like adversarial training) impose debiasing by updating a model’s parameters, …...

AbstractCourt Judgement Prediction with Explanation (CJPE) is a task in the field of legal analysis and evaluation, which involves predicting the outcome of a court case based on the available …...