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Found 14 papers, 5 papers with code
"One-Size-Fits-All"? Examining Expectations around What Constitute "Fair" or "Good" NLG System Behaviors
Fairness-related assumptions about what constitute appropriate NLG system behaviors range from invariance, where systems are expected to behave identically for social groups, to adaptation, where behaviors should instead vary across them.
PREME: Preference-based Meeting Exploration through an Interactive Questionnaire
The recent increase in the volume of online meetings necessitates automated tools for managing and organizing the material, especially when an attendee has missed the discussion and needs assistance in quickly exploring it.
Knowledge Infused Decoding
Hence, they tend to suffer from counterfactual or hallucinatory generation when used in knowledge-intensive natural language generation (NLG) tasks.
Ranked #2 on
Question Answering
on KILT: ELI5
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding
Recent work has focused on compressing pre-trained language models (PLMs) like BERT where the major focus has been to improve the in-distribution performance for downstream tasks.
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis
As such, these models are often unable to produce personalized responses for individual users, based on their data.
A Conditional Generative Matching Model for Multi-lingual Reply Suggestion
We study the problem of multilingual automated reply suggestions (RS) model serving many languages simultaneously.
Language Scaling for Universal Suggested Replies Model
Faced with increased compute requirements and low resources for language expansion, we build a single universal model for improving the quality and reducing run-time costs of our production system.
A Dataset and Baselines for Multilingual Reply Suggestion
Reply suggestion models help users process emails and chats faster.
When does text prediction benefit from additional context? An exploration of contextual signals for chat and email messages
We compare contextual text prediction in chat and email messages from two of the largest commercial platforms Microsoft Teams and Outlook, finding that contextual signals contribute to performance differently between these scenarios.
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning
Extensive experiments on real-world low-resource languages - without access to large-scale monolingual corpora or large amounts of labeled data - for tasks like cross-lingual sentiment analysis and named entity recognition show the effectiveness of our approach.
Learning with Weak Supervision for Email Intent Detection
In this paper, we propose to leverage user actions as a source of weak supervision, in addition to a limited set of annotated examples, to detect intents in emails.
Differentially Private Set Union
Known algorithms for this problem proceed by collecting a subset of items from each user, taking the union of such subsets, and disclosing the items whose noisy counts fall above a certain threshold.
Characterizing Reading Time on Enterprise Emails
Our approach starts with a reading time analysis based on the reading events from a major email platform, followed by a user study to provide explanations for some discoveries.
Diversifying Reply Suggestions using a Matching-Conditional Variational Autoencoder
We consider the problem of diversifying automated reply suggestions for a commercial instant-messaging (IM) system (Skype).
