Articles

Stereotypes are inferences drawn about people based on their demographic attributes, which may result in harms to users when a system is deployed. In generative language-inference tasks, given a premise, a model produces plausible hypotheses that follow either logi- cally (natural language inference) or common- sensically (commonsense inference). Such tasks …...

Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks. How- ever, KG edge (fact) sparsity and noisy edge extraction/generation often hinder mod- els from obtaining useful knowledge to rea- son over. To address these issues, we propose a new KG-augmented model: Hybrid Graph Network …...

We computationally model the processes of word borrowing from a donor word to an in- corporated word, and vice versa, by answer- ing two questions: (1) what does a word look like incorporated into another language, and in the opposite direction (2) where did a word come from? We employ …...

In intensive care units (ICUs), patient health is monitored through (1) continuous vital signals from various medical devices, and (2) clinical notes consisting of opinions and summaries from doctors which are recorded in electronic health records (EHR). It is difficult to jointly model these two sources of information be- cause …...

Neural network algorithms such as those based on transformers and attention models have ex- celled on Automatic Text Classification (ATC) tasks. However, such enhanced performance comes at high computational costs. Ensem- bles of simpler classifiers (i.e., stacking) that exploit algorithmic and representational complementarities have also been shown to produce top-notch …...

Recent papers have introduced methods to in- corporate gazetteer features and entity segmen- tation techniques in neural named entity recog- nition models. These papers rely on differ- ent resources and include features not related to the use of gazetteers, rendering impossible the comparison of the relative effectiveness of the approaches. …...

With the increasing interest in low-resource languages, unsupervised morphological seg- mentation has become an active area of re- search, where approaches based on Adap- tor Grammars achieve state-of-the-art results. We demonstrate the power of harnessing lin- guistic knowledge as priors within Adaptor Grammars in a minimally-supervised learning fashion. We introduce …...

Detecting what emotions are expressed in text is a well-studied problem in natural lan- guage processing. However, research on finer- grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in a joint fash- …...

Transformer has become ubiquitous in the deep learning field. One of the key ingredients that destined its success is the self-attention mechanism, which allows fully-connected con- textual encoding over input tokens. However, despite its effectiveness in modeling short se- quences, self-attention suffers when handling inputs with extreme long-range dependencies, as …...

Following procedural texts written in natural languages is challenging. We must read the whole text to identify the relevant informa- tion or identify the instruction-flow to com- plete a task, which is prone to failures. If such texts are structured, we can readily visu- alize instruction-flows, reason or infer a …...

Due to the multi-dimensional variation of tex- tual data, detection of event triggers from new domains can become a lot more challenging. This prompts a need to research on domain adaptation methods for event detection task, especially for the most practical unsupervised setting. Recently, large transformer-based lan- guage models, e.g. …...

Many types of text style transfer can be achieved with only small, precise edits (e.g. sentiment transfer from I had a terrible time... to I had a great time...). We propose a coarse-to-fine editor for style transfer that transforms text using Levenshtein edit operations (e.g. insert, replace, delete). Unlike prior …...

State of the art end-to-end coreference reso- lution models use expensive span representa- tions and antecedent prediction mechanisms. These approaches are expensive both in terms of their memory requirements as well as com- pute time, and are particularly ill-suited for long documents. In this paper, we propose an approximation to …...

We introduce a novel dataset of human judg- ments of machine-edited text and initial mod- els of those perceptions. Six machine-editing methods ranging from character swapping to variational autoencoders are applied to collec- tions of English-language social media text and scientific abstracts. The edits are judged in context for detectability …...

With the rapid growth of social media in the past decade, the news are no longer controlled by just a few mainstream sources. Users them- selves create large numbers of potentially ficti- tious rumours, necessitating automated verac- ity classification systems. Here we present a novel approach towards automatically classi- fying …...

Meaning Representation for Knowledge Base Question Answering Pavan Kapanipathi∗, Ibrahim Abdelaziz∗, Srinivas Ravishankar∗, Salim Roukos, Alexander Gray, Ramon Astudillo, Maria Chang, Cristina Cornelio, Saswati Dana, Achille Fokoue, Dinesh Garg, Alfio Gliozzo, Sairam Gurajada, Hima Karanam, Naweed Khan, Dinesh Khandelwal, Young-Suk Lee, Yunyao Li, Francois Luus, Ndivhuwo Makondo, Nandana Mihindukulasooriya, Tahira …...

Open-domain neural dialogue models have achieved high performance in response rank- ing and evaluation tasks. These tasks are formulated as a binary classification of re- sponses given in a dialogue context, and mod- els generally learn to make predictions based on context-response content similarity. How- ever, over-reliance on content similarity …...

Humans have been shown to give contrastive explanations, which explain why an observed event happened rather than some other coun- terfactual event (the contrast case). De- spite the influential role that contrastivity plays in how humans explain, this property is largely missing from current methods for explaining NLP models. We …...

Weakly supervised methods estimate the la- bels for a dataset using the predictions of sev- eral noisy supervision sources. Many machine learning practitioners have begun using weak supervision to more quickly and cheaply an- notate data compared to traditional manual la- beling. In this paper, we focus on the spe- …...

Larger language models have higher accu- racy on average, but are they better on ev- ery single instance (datapoint)? Some work suggests larger models have higher out-of- distribution robustness, while other work sug- gests they have lower accuracy on rare sub- groups. To understand these differences, we investigate these models …...