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Found 33 papers, 20 papers with code
On the Effect of Isotropy on VAE Representations of Text
Injecting desired geometric properties into text representations has attracted a lot of attention.
Integrating Transformers and Knowledge Graphs for Twitter Stance Detection
Stance detection (SD) entails classifying the sentiment of a text towards a given target, and is a relevant sub-task for opinion mining and social media analysis.
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games
Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces.
Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation
Translating natural language sentences to first-order logic (NL-FOL translation) is a longstanding challenge in the NLP and formal logic literature.
PiVe: Prompting with Iterative Verification Improving Graph-based Generative Capability of LLMs
Large language models (LLMs) have shown great abilities of solving various natural language tasks in different domains.
A Minimal Approach for Natural Language Action Space in Text-based Games
Text-based games (TGs) are language-based interactive environments for reinforcement learning.
Koala: An Index for Quantifying Overlaps with Pre-training Corpora
In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour.
Plug-and-Play Recipe Generation with Content Planning
Specifically, it optimizes the joint distribution of the natural language sequence and the global content plan in a plug-and-play manner.
Self-supervised Rewiring of Pre-trained Speech Encoders: Towards Faster Fine-tuning with Less Labels in Speech Processing
Pre-trained speech Transformers have facilitated great success across various speech processing tasks.
Self-supervised Graph Masking Pre-training for Graph-to-Text Generation
Large-scale pre-trained language models (PLMs) have advanced Graph-to-Text (G2T) generation by processing the linearised version of a graph.
RedApt: An Adaptor for wav2vec 2 Encoding \\ Faster and Smaller Speech Translation without Quality Compromise
Pre-trained speech Transformers in speech translation (ST) have facilitated state-of-the-art (SotA) results; yet, using such encoders is computationally expensive.
Generating Synthetic Speech from SpokenVocab for Speech Translation
Training end-to-end speech translation (ST) systems requires sufficiently large-scale data, which is unavailable for most language pairs and domains.
On Reality and the Limits of Language Data: Aligning LLMs with Human Norms
Recent advancements in Large Language Models (LLMs) harness linguistic associations in vast natural language data for practical applications.
M-Adapter: Modality Adaptation for End-to-End Speech-to-Text Translation
End-to-end speech-to-text translation models are often initialized with pre-trained speech encoder and pre-trained text decoder.
TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning
Masked language models (MLMs) such as BERT and RoBERTa have revolutionized the field of Natural Language Understanding in the past few years.
Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models
To catalyse the research in this direction, we release a well-curated biomedical knowledge probing benchmark, MedLAMA, which is constructed based on the Unified Medical Language System (UMLS) Metathesaurus.
The Neglected Sibling: Isotropic Gaussian Posterior for VAE
Deep generative models have been widely used in several areas of NLP, and various techniques have been proposed to augment them or address their training challenges.
It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data
Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora.
Mixture-of-Partitions: Infusing Large Biomedical Knowledge Graphs into BERT
Infusing factual knowledge into pre-trained models is fundamental for many knowledge-intensive tasks.
Unsupervised Representation Disentanglement of Text: An Evaluation on Synthetic Datasets
To highlight the challenges of achieving representation disentanglement for text domain in an unsupervised setting, in this paper we select a representative set of successfully applied models from the image domain.
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification
Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP.
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters
Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT.
Self-Alignment Pretraining for Biomedical Entity Representations
Despite the widespread success of self-supervised learning via masked language models (MLM), accurately capturing fine-grained semantic relationships in the biomedical domain remains a challenge.
COMETA: A Corpus for Medical Entity Linking in the Social Media
Whilst there has been growing progress in Entity Linking (EL) for general language, existing datasets fail to address the complex nature of health terminology in layman's language.
Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders
It has been long known that sparsity is an effective inductive bias for learning efficient representation of data in vectors with fixed dimensionality, and it has been explored in many areas of representation learning.
On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation
While the explicit constraint naturally avoids posterior collapse, we use it to further understand the significance of the KL term in controlling the information transmitted through the VAE channel.
Bayesian Learning for Neural Dependency Parsing
While neural dependency parsers provide state-of-the-art accuracy for several languages, they still rely on large amounts of costly labeled training data.
Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines
In recent years neural language models (LMs) have set the state-of-the-art performance for several benchmarking datasets.
Fast, Small and Exact: Infinite-order Language Modelling with Compressed Suffix Trees
Efficient methods for storing and querying are critical for scaling high-order n-gram language models to large corpora.
Structured Prediction of Sequences and Trees using Infinite Contexts
Linguistic structures exhibit a rich array of global phenomena, however commonly used Markov models are unable to adequately describe these phenomena due to their strong locality assumptions.
