Search Results for author: Karl Stratos

Found 37 papers, 17 papers with code

EntQA: Entity Linking as Question Answering

1 code implementation ICLR 2022 Wenzheng Zhang, Wenyue Hua, Karl Stratos

A conventional approach to entity linking is to first find mentions in a given document and then infer their underlying entities in the knowledge base.

Benchmarking Entity Linking +4

Fast and Effective Biomedical Entity Linking Using a Dual Encoder

1 code implementation EACL (Louhi) 2021 Rajarshi Bhowmik, Karl Stratos, Gerard de Melo

Additionally, we modify our dual encoder model for end-to-end biomedical entity linking that performs both mention span detection and entity disambiguation and out-performs two recently proposed models.

Entity Disambiguation Entity Linking

Data-to-text Generation by Splicing Together Nearest Neighbors

1 code implementation EMNLP 2021 Sam Wiseman, Arturs Backurs, Karl Stratos

We propose to tackle data-to-text generation tasks by directly splicing together retrieved segments of text from "neighbor" source-target pairs.

Conditional Text Generation Data-to-Text Generation

Corrected CBOW Performs as well as Skip-gram

1 code implementation EMNLP (insights) 2021 Ozan İrsoy, Adrian Benton, Karl Stratos

Mikolov et al. (2013a) observed that continuous bag-of-words (CBOW) word embeddings tend to underperform Skip-gram (SG) embeddings, and this finding has been reported in subsequent works.

Word Embeddings

Unsupervised Label Refinement Improves Dataless Text Classification

1 code implementation Findings (ACL) 2021 Zewei Chu, Karl Stratos, Kevin Gimpel

This reliance causes dataless classifiers to be highly sensitive to the choice of label descriptions and hinders the broader application of dataless classification in practice.

General Classification Text Classification +1

NatCat: Weakly Supervised Text Classification with Naturally Annotated Resources

1 code implementation AKBC 2021 Zewei Chu, Karl Stratos, Kevin Gimpel

We describe NatCat, a large-scale resource for text classification constructed from three data sources: Wikipedia, Stack Exchange, and Reddit.

General Classification Text Categorization +1

Discrete Latent Variable Representations for Low-Resource Text Classification

1 code implementation ACL 2020 Shuning Jin, Sam Wiseman, Karl Stratos, Karen Livescu

While much work on deep latent variable models of text uses continuous latent variables, discrete latent variables are interesting because they are more interpretable and typically more space efficient.

General Classification Sentence Classification +1

Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information

1 code implementation ICML 2020 Karl Stratos, Sam Wiseman

We propose learning discrete structured representations from unlabeled data by maximizing the mutual information between a structured latent variable and a target variable.

Label-Agnostic Sequence Labeling by Copying Nearest Neighbors

1 code implementation ACL 2019 Sam Wiseman, Karl Stratos

Retrieve-and-edit based approaches to structured prediction, where structures associated with retrieved neighbors are edited to form new structures, have recently attracted increased interest.

Structured Prediction

Formal Limitations on the Measurement of Mutual Information

2 code implementations ICLR 2019 David McAllester, Karl Stratos

Measuring mutual information from finite data is difficult.

Mutual Information Maximization for Simple and Accurate Part-Of-Speech Induction

1 code implementation NAACL 2019 Karl Stratos

We address part-of-speech (POS) induction by maximizing the mutual information between the induced label and its context.


OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding

no code implementations16 Jan 2018 Young-Bum Kim, Sungjin Lee, Karl Stratos

In practice, most spoken language understanding systems process user input in a pipelined manner; first domain is predicted, then intent and semantic slots are inferred according to the semantic frames of the predicted domain.

Spoken Language Understanding

Reconstruction of Word Embeddings from Sub-Word Parameters

no code implementations WS 2017 Karl Stratos

Pre-trained word embeddings improve the performance of a neural model at the cost of increasing the model size.

Part-Of-Speech Tagging Word Embeddings +1

A Sub-Character Architecture for Korean Language Processing

1 code implementation EMNLP 2017 Karl Stratos

We introduce a novel sub-character architecture that exploits a unique compositional structure of the Korean language.

Dependency Parsing

Adversarial Adaptation of Synthetic or Stale Data

no code implementations ACL 2017 Young-Bum Kim, Karl Stratos, Dongchan Kim

Both cause a distribution mismatch between training and evaluation, leading to a model that overfits the flawed training data and performs poorly on the test data.

Domain Adaptation Spoken Language Understanding

Domain Attention with an Ensemble of Experts

no code implementations ACL 2017 Young-Bum Kim, Karl Stratos, Dongchan Kim

When given domain K + 1, our model uses a weighted combination of the K domain experts{'} feedback along with its own opinion to make predictions on the new domain.

Domain Adaptation Spoken Language Understanding

Entity Identification as Multitasking

1 code implementation WS 2017 Karl Stratos

Standard approaches in entity identification hard-code boundary detection and type prediction into labels (e. g., John/B-PER Smith/I-PER) and then perform Viterbi.

Boundary Detection Type prediction +1

Frustratingly Easy Neural Domain Adaptation

no code implementations COLING 2016 Young-Bum Kim, Karl Stratos, Ruhi Sarikaya

Popular techniques for domain adaptation such as the feature augmentation method of Daum{\'e} III (2009) have mostly been considered for sparse binary-valued features, but not for dense real-valued features such as those used in neural networks.

Domain Adaptation

Domainless Adaptation by Constrained Decoding on a Schema Lattice

no code implementations COLING 2016 Young-Bum Kim, Karl Stratos, Ruhi Sarikaya

In many applications such as personal digital assistants, there is a constant need for new domains to increase the system{'}s coverage of user queries.

Multi-Label Classification Spoken Language Understanding

Unsupervised Part-Of-Speech Tagging with Anchor Hidden Markov Models

1 code implementation TACL 2016 Karl Stratos, Michael Collins, Daniel Hsu

We tackle unsupervised part-of-speech (POS) tagging by learning hidden Markov models (HMMs) that are particularly well-suited for the problem.


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