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Found 17 papers, 14 papers with code
DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction
We name this scheme DILBERT: Domain Invariant Learning with BERT, and customize it for aspect extraction in the unsupervised domain adaptation setting.
Are Large Language Models Temporally Grounded?
Instead, we provide LLMs with textual narratives and probe them with respect to their common-sense knowledge of the structure and duration of events, their ability to order events along a timeline, and self-consistency within their temporal model (e. g., temporal relations such as after and before are mutually exclusive for any pair of events).
A Joint Matrix Factorization Analysis of Multilingual Representations
Using this tool, we study to what extent and how morphosyntactic features are reflected in the representations learned by multilingual pre-trained models.
Detecting and Mitigating Hallucinations in Multilingual Summarisation
With the existing faithful metrics focusing on English, even measuring the extent of this phenomenon in cross-lingual settings is hard.
BERT is not The Count: Learning to Match Mathematical Statements with Proofs
While the first decoding method matches a proof to a statement without being aware of other statements or proofs, the second method treats the task as a global matching problem.
Erasure of Unaligned Attributes from Neural Representations
We present the Assignment-Maximization Spectral Attribute removaL (AMSAL) algorithm, which erases information from neural representations when the information to be erased is implicit rather than directly being aligned to each input example.
Understanding Domain Learning in Language Models Through Subpopulation Analysis
We investigate how different domains are encoded in modern neural network architectures.
Domain Adaptation from Scratch
In this setup, we aim to efficiently annotate data from a set of source domains such that the trained model performs well on a sensitive target domain from which data is unavailable for annotation.
Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents
We argue that disentangling content selection from the budget used to cover salient content improves the performance and applicability of abstractive summarizers.
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Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information
We describe a simple and effective method (Spectral Attribute removaL; SAL) to remove private or guarded information from neural representations.
DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect Extraction
We name this scheme DILBERT: Domain Invariant Learning with BERT, and customize it for aspect extraction in the unsupervised domain adaptation setting.
WikiSum: Coherent Summarization Dataset for Efficient Human-Evaluation
Recent works made significant advances on summarization tasks, facilitated by summarization datasets.
Answering Product-Questions by Utilizing Questions from Other Contextually Similar Products
In this work, we propose a novel and complementary approach for predicting the answer for such questions, based on the answers for similar questions asked on similar products.
Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation
Pivot Based Language Modeling (PBLM) (Ziser and Reichart, 2018a), combining LSTMs with pivot-based methods, has yielded significant progress in unsupervised domain adaptation.
Deep Pivot-Based Modeling for Cross-language Cross-domain Transfer with Minimal Guidance
In the full setup the model has access to unlabeled data from both pairs, while in the lazy setup, which is more realistic for truly resource-poor languages, unlabeled data is available for both domains but only for the source language.
Pivot Based Language Modeling for Improved Neural Domain Adaptation
Particularly, our model processes the information in the text with a sequential NN (LSTM) and its output consists of a representation vector for every input word.
Neural Structural Correspondence Learning for Domain Adaptation
Particularly, our model is a three-layer neural network that learns to encode the nonpivot features of an input example into a low-dimensional representation, so that the existence of pivot features (features that are prominent in both domains and convey useful information for the NLP task) in the example can be decoded from that representation.
