Articles

ive Summarizers Paraphrase Text Matt Wilber Cornell University mw979@cornell.edu William Timkey Cornell University wpt25@cornell.edu Marten van Schijndel Cornell University mv443@cornell.edu Abstract Abstractive neural summarization models have seen great improvements in recent years, as shown by ROUGE scores of the gener- ated summaries. But despite these improved metrics, there is limited …...

In Grammatical Error Correction (GEC), se- quence labeling models enjoy fast inference compared to sequence-to-sequence models; however, inference in sequence labeling GEC models is an iterative process, as sentences are passed to the model for multiple rounds of cor- rection, which exposes the model to sentences with progressively fewer errors …...

Information Extraction (IE) for semi- structured document images is often ap- proached as a sequence tagging problem by classifying each recognized input token into one of the IOB (Inside, Outside, and Beginning) categories. However, such prob- lem setup has two inherent limitations that (1) it cannot easily handle complex spatial …...

Pre-trained contextualized language models (PrLMs) have led to strong performance gains in downstream natural language understand- ing tasks. However, PrLMs can still be easily fooled by adversarial word substitution, which is one of the most challenging textual adver- sarial attack methods. Existing defence ap- proaches suffer from notable performance loss …...

The combined use of neural scoring systems and BERT fine-tuning has led to very high results in many natural language processing (NLP) tasks. These high results raise two im- portant questions about the contribution and the limitations of pretrained-language models: (i) what are the remaining errors in the best- performing …...

Language generation models’ democratiza- tion benefits many domains, from answer- ing health-related questions to enhancing ed- ucation by providing AI-driven tutoring ser- vices. However, language generation mod- els’ democratization also makes it easier to generate human-like text at-scale for nefari- ous activities, from spreading misinformation to targeting specific groups with …...

Recent studies have suggested that weight pruning, e.g. using lottery ticket extraction techniques (Frankle and Carbin, 2018), comes at the risk of compromising the group fairness of machine learning models (Paganini, 2020; Hooker et al., 2020), but to the best of our knowledge, no one has empirically evaluated this hypothesis …...

In this paper, we investigate few-shot joint learning for dialogue language understanding. Most existing few-shot models learn a sin- gle task each time with only a few exam- ples. However, dialogue language understand- ing contains two closely related tasks, i.e., in- tent detection and slot filling, and often bene- fits …...

Improving the robustness of neural machine translation models on variations of input sen- tences is an active area of research. In this paper, we propose a simple data augmenta- tion approach by sampling virtual sentences from the vicinity distributions in higher-level representations, constructed either from indi- vidual training samples via …...

Indonesian language is heavily riddled with colloquialism whether in written or spoken forms. In this paper, we identify a class of Indonesian colloquial words that have un- dergone morphological transformations from their standard forms, categorize their word for- mations, and propose a benchmark dataset of Indonesian Colloquial Lexicons (IndoCollex) consisting …...

Despite recent advances, standard sequence labeling systems often fail when processing noisy user-generated text or consuming the output of an Optical Character Recognition (OCR) process. In this paper, we improve the noise-aware training method by proposing an empirical error generation approach that em- ploys a sequence-to-sequence model trained to perform …...

In this paper, we consider a realistic scenario on stance detection with more application po- tential, i.e., zero-shot and few-shot stance de- tection, which identifies stances for a wide range of topics with no or very few training ex- amples. Conventional data-driven approaches are not applicable to the above zero-shot …...

In this paper, we investigate the problem of reasoning over natural language statements. Prior neural based approaches do not explic- itly consider the inter-dependency among an- swers and their proofs. In this paper, we pro- pose PROBR, a novel approach for joint an- swer prediction and proof generation. PROBR defines …...

Data augmentation with mixup has shown to be effective on various computer vision tasks. Despite its great success, there has been a hurdle to apply mixup to NLP tasks since text consists of discrete tokens with variable length. In this work, we propose SSMix, a novel mixup method where the …...

Sequences are typically decoded in a left- to-right fashion, requiring as many decoding steps as there are tokens in the sequence. Recently, several works have proposed non- autoregressive decoders that are sub-linear, al- lowing to decode a sequence using fewer de- coding steps than the length of the sequence, and …...

We propose a Bi-Directional Manifold Align- ment (BDMA) that learns a non-linear map- ping between two manifolds by explicitly training it to be bijective. We demonstrate BDMA by training a model for a pair of lan- guages rather than individual, directed source and target combinations, reducing the num- ber of …...

ive Summarization by Incorporating Answer Relevance Dan Su∗, Tiezheng Yu∗, Pascale Fung Center for Artificial Intelligence Research (CAiRE) Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong {dsu,tyuah}@connect.ust.hk, pascale@ece.ust.hk Abstract Query focused summarization (QFS) models aim to generate summaries from …...

In this work, we propose a new task call- ed Image-guided Story Ending Generation (IgSEG). Given a multi-sentence story plot and an ending-related image, IgSEG aims to gen- erate a story ending that conforms to the con- textual logic and the relevant visual concepts. In contrast to the story ending …...

Recent years have seen many breakthroughs in natural language processing (NLP), transi- tioning it from a mostly theoretical field to one with many real-world applications. Noting the rising number of applications of other machine learning and AI techniques with pervasive so- cietal impact, we anticipate the rising impor- tance of …...

We present a novel model for the problem of ranking a collection of documents according to their semantic similarity to a source (query) document. While the problem of document-to- document similarity ranking has been studied, most modern methods are limited to relatively short documents or rely on the existence of …...