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Found 21 papers, 6 papers with code
Findings of the WOAH 5 Shared Task on Fine Grained Hateful Memes Detection
We present the results and main findings of the shared task at WOAH 5 on hateful memes detection.
Prompt-free and Efficient Few-shot Learning with Language Models
Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM can score.
Meta-training with Demonstration Retrieval for Efficient Few-shot Learning
Meta-training allows one to leverage smaller models for few-shot generalization in a domain-general and task-agnostic manner; however, these methods alone results in models that may not have sufficient parameterization or knowledge to adapt quickly to a large variety of tasks.
TimelineQA: A Benchmark for Question Answering over Timelines
We describe a set of experiments on TimelineQA with several state-of-the-art QA models.
Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI
Evaluating an explanation's faithfulness is desired for many reasons such as trust, interpretability and diagnosing the sources of model's errors.
ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection
Hate speech detection is complex; it relies on commonsense reasoning, knowledge of stereotypes, and an understanding of social nuance that differs from one culture to the next.
Cultural Vocal Bursts Intensity Prediction
Few-Shot Learning
+1
Policy Compliance Detection via Expression Tree Inference
In this work, we learn how to infer expression trees automatically from policy texts.
PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models
Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM can score.
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
An extractive rationale explains a language model's (LM's) prediction on a given task instance by highlighting the text inputs that most influenced the prediction.
UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuning
Recent parameter-efficient language model tuning (PELT) methods manage to match the performance of fine-tuning with much fewer trainable parameters and perform especially well when training data is limited.
Personalized Query Rewriting in Conversational AI Agents
Spoken language understanding (SLU) systems in conversational AI agents often experience errors in the form of misrecognitions by automatic speech recognition (ASR) or semantic gaps in natural language understanding (NLU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Pre-Training for Query Rewriting in A Spoken Language Understanding System
Then, inspired by the wide success of pre-trained contextual language embeddings, and also as a way to compensate for insufficient QR training data, we propose a language-modeling (LM) based approach to pre-train query embeddings on historical user conversation data with a voice assistant.
Leveraging External Knowledge for Out-Of-Vocabulary Entity Labeling
This network projects the slot into an attribute space derived from the KB, and, by leveraging similarities in this space, we propose candidate slot keys and values to the dialogue state tracker.
Time Masking: Leveraging Temporal Information in Spoken Dialogue Systems
Much of the previous work has relied on modeling the natural order of the conversation, using distance based offsets as an approximation of time.
Ranked #6 on
Video Salient Object Detection
on SegTrack v2
(using extra training data)
Improving Long Distance Slot Carryover in Spoken Dialogue Systems
One such approach for tracking the dialogue state is slot carryover, where a model makes a binary decision if a slot from the context is relevant to the current turn.
A dataset for resolving referring expressions in spoken dialogue via contextual query rewrites (CQR)
In this paper, we describe our methodology for creating the query reformulation extension to the dialog corpus, and present an initial set of experiments to establish a baseline for the CQR task.
Scaling Multi-Domain Dialogue State Tracking via Query Reformulation
We present a novel approach to dialogue state tracking and referring expression resolution tasks.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+3
Cross-Lingual Approaches to Reference Resolution in Dialogue Systems
In the cross-lingual setup, we assume there is access to annotated resources as well as a well trained model in the source language and little to no annotated data in the target language.
Contextual Slot Carryover for Disparate Schemas
In the slot-filling paradigm, where a user can refer back to slots in the context during a conversation, the goal of the contextual understanding system is to resolve the referring expressions to the appropriate slots in the context.
The Alexa Meaning Representation Language
This paper introduces a meaning representation for spoken language understanding.
Transfer Learning for Neural Semantic Parsing
The goal of semantic parsing is to map natural language to a machine interpretable meaning representation language (MRL).
