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Found 16 papers, 3 papers with code
MTL-SLT: Multi-Task Learning for Spoken Language Tasks
The proposed framework benefits from three key aspects: 1) pre-trained sub-networks of ASR model and language model; 2) multi-task learning objective to exploit shared knowledge from different tasks; 3) end-to-end training of ASR and downstream NLP task based on sequence loss.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Mask-ControlNet: Higher-Quality Image Generation with An Additional Mask Prompt
Then, the object images are employed as additional prompts to facilitate the diffusion model to better understand the relationship between foreground and background regions during image generation.
Are Large Language Models Really Robust to Word-Level Perturbations?
Notably, we are surprised to discover that robustness tends to decrease as fine-tuning (SFT and RLHF) is conducted.
Soft Prompt Decoding for Multilingual Dense Retrieval
In this work, we explore a Multilingual Information Retrieval (MLIR) task, where the collection includes documents in multiple languages.
Cross-Lingual Information Retrieval
Knowledge Distillation
+1
Cross-lingual Knowledge Transfer via Distillation for Multilingual Information Retrieval
In this paper, we introduce the approach behind our submission for the MIRACL challenge, a WSDM 2023 Cup competition that centers on ad-hoc retrieval across 18 diverse languages.
Improving Cross-lingual Information Retrieval on Low-Resource Languages via Optimal Transport Distillation
Moreover, unlike the English-to-English retrieval task, where large-scale training collections for document ranking such as MS MARCO are available, the lack of cross-lingual retrieval data for low-resource language makes it more challenging for training cross-lingual retrieval models.
Towards Joint Intent Detection and Slot Filling via Higher-order Attention
Intent detection (ID) and Slot filling (SF) are two major tasks in spoken language understanding (SLU).
Mixed Attention Transformer for Leveraging Word-Level Knowledge to Neural Cross-Lingual Information Retrieval
In the monolingual retrieval task, because of the same lexical inputs, it is easier for model to identify the query terms that occurred in documents.
HAN: Higher-order Attention Network for Spoken Language Understanding
Spoken Language Understanding (SLU), including intent detection and slot filling, is a core component in human-computer interaction.
GhostBERT: Generate More Features with Cheap Operations for BERT
Transformer-based pre-trained language models like BERT, though powerful in many tasks, are expensive in both memory and computation, due to their large number of parameters.
Audio-Oriented Multimodal Machine Comprehension: Task, Dataset and Model
As a result, the proposed approach can handle various tasks including: Audio-Oriented Multimodal Machine Comprehension, Machine Reading Comprehension and Machine Listening Comprehension, in a single model, making fair comparisons possible between our model and the existing unimodal MC models.
Federated Learning for Spoken Language Understanding
To this end, we propose a federated learning framework, which could unify various types of datasets as well as tasks to learn and fuse various types of knowledge, i. e., text representations, from different datasets and tasks, without the sharing of downstream task data.
PIN: A Novel Parallel Interactive Network for Spoken Language Understanding
However, we noted that, so far, the efforts to obtain better performance by supporting bidirectional and explicit information exchange between ID and SF are not well studied. In addition, few studies attempt to capture the local context information to enhance the performance of SF.
DynaBERT: Dynamic BERT with Adaptive Width and Depth
The pre-trained language models like BERT, though powerful in many natural language processing tasks, are both computation and memory expensive.
The Simons Observatory: Science goals and forecasts
With up to an order of magnitude lower polarization noise than maps from the Planck satellite, the high-resolution sky maps will constrain cosmological parameters derived from the damping tail, gravitational lensing of the microwave background, the primordial bispectrum, and the thermal and kinematic Sunyaev-Zel'dovich effects, and will aid in delensing the large-angle polarization signal to measure the tensor-to-scalar ratio.
Cosmology and Nongalactic Astrophysics
The CMB bispectrum from recombination
We compute the cosmic microwave background temperature bispectrum generated by nonlinearities at recombination on all scales.
Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology High Energy Physics - Phenomenology High Energy Physics - Theory 83F05 J.2
