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Found 50 papers, 21 papers with code
What Does Your Smile Mean? Jointly Detecting Multi-Modal Sarcasm and Sentiment Using Quantum Probability
The QPM framework involves a complex-valued multi-modal representation encoder, a quantum-like fusion subnetwork and a quantum measurement mechanism.
Beyond Emotion: A Multi-Modal Dataset for Human Desire Understanding
Desire is a strong wish to do or have something, which involves not only a linguistic expression, but also underlying cognitive phenomena driving human feelings.
LLM-Oriented Retrieval Tuner
Dense Retrieval (DR) is now considered as a promising tool to enhance the memorization capacity of Large Language Models (LLM) such as GPT3 and GPT-4 by incorporating external memories.
Towards the Law of Capacity Gap in Distilling Language Models
The pain is mainly resulted by the curse of capacity gap, which describes that a larger teacher LM cannot always lead to a better student LM than one distilled from a smaller teacher LM due to the affect of capacity gap increment.
On Elastic Language Models
To this end, we propose an elastic language model (ElasticLM) that elastically adjusts the tradeoff according to the request stream.
Sparse Contrastive Learning of Sentence Embeddings
Recently, SimCSE has shown the feasibility of contrastive learning in training sentence embeddings and illustrates its expressiveness in spanning an aligned and uniform embedding space.
Controllable Text Generation with Residual Memory Transformer
Large-scale Causal Language Models (CLMs), e. g., GPT3 and ChatGPT, have brought great success in text generation.
A Survey of Quantum-Cognitively Inspired Sentiment Analysis Models
Quantum theory, originally proposed as a physical theory to describe the motions of microscopic particles, has been applied to various non-physics domains involving human cognition and decision-making that are inherently uncertain and exhibit certain non-classical, quantum-like characteristics.
A Quantum Probability Driven Framework for Joint Multi-Modal Sarcasm, Sentiment and Emotion Analysis
In view of the recent success of quantum probability (QP) in modeling human cognition, particularly contextual incompatible decision making, we take the first step towards introducing QP into joint multi-modal sarcasm, sentiment, and emotion analysis.
Task-agnostic Distillation of Encoder-Decoder Language Models
Finetuning pretrained language models (LMs) have enabled appealing performance on a diverse array of tasks.
Lifting the Curse of Capacity Gap in Distilling Language Models
However, when the capacity gap between the teacher and the student is large, a curse of capacity gap appears, invoking a deficiency in distilling LMs.
Modular Retrieval for Generalization and Interpretation
Built upon the paradigm, we propose a retrieval model with modular prompt tuning named REMOP.
DisCup: Discriminator Cooperative Unlikelihood Prompt-tuning for Controllable Text Generation
Prompt learning with immensely large Casual Language Models (CLMs) has been shown promising for attribute-controllable text generation (CTG).
XPrompt: Exploring the Extreme of Prompt Tuning
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for performing downstream tasks in a parameter-efficient manner.
Sparse Teachers Can Be Dense with Knowledge
Recent advances in distilling pretrained language models have discovered that, besides the expressiveness of knowledge, the student-friendliness should be taken into consideration to realize a truly knowledgable teacher.
Structural Bias for Aspect Sentiment Triplet Extraction
Thus, a natural question arises: Is structural bias still a necessity in the context of PLMs?
Doge Tickets: Uncovering Domain-general Language Models by Playing Lottery Tickets
To uncover the domain-general LM, we propose to identify domain-general parameters by playing lottery tickets (dubbed doge tickets).
Aspect-specific Context Modeling for Aspect-based Sentiment Analysis
Extensive experimental results on standard and adversarial benchmarks for SC and OE demonstrate the effectiveness and robustness of the proposed method, yielding new state-of-the-art performance on OE and competitive performance on SC.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
A Multibias-mitigated and Sentiment Knowledge Enriched Transformer for Debiasing in Multimodal Conversational Emotion Recognition
Multimodal emotion recognition in conversations (mERC) is an active research topic in natural language processing (NLP), which aims to predict human's emotional states in communications of multiple modalities, e, g., natural language and facial gestures.
MiniDisc: Minimal Distillation Schedule for Language Model Compression
In particular, motivated by the finding that the performance of the student is positively correlated to the scale-performance tradeoff of the teacher assistant, MiniDisc is designed with a $\lambda$-tradeoff to measure the optimality of the teacher assistant without trial distillation to the student.
Making Pretrained Language Models Good Long-tailed Learners
Prompt-tuning has shown appealing performance in few-shot classification by virtue of its capability in effectively exploiting pre-trained knowledge.
Adaptable Text Matching via Meta-Weight Regulator
Neural text matching models have been used in a range of applications such as question answering and natural language inference, and have yielded a good performance.
EEG based Emotion Recognition: A Tutorial and Review
Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic.
A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models
In recent years, methods using large-scale pre-trained language models (PLMs), in particular the widely used transformer-based PLMs, have become a new paradigm of NLG, allowing generation of more diverse and fluent text.
Exploiting Position Bias for Robust Aspect Sentiment Classification
Aspect sentiment classification (ASC) aims at determining sentiments expressed towards different aspects in a sentence.
Quantum Cognitively Motivated Decision Fusion for Video Sentiment Analysis
Thus the cognitive process exhibits "quantum-like" biases that cannot be captured by classical probability theories.
A Multi-task Learning Framework for Opinion Triplet Extraction
The state-of-the-art Aspect-based Sentiment Analysis (ABSA) approaches are mainly based on either detecting aspect terms and their corresponding sentiment polarities, or co-extracting aspect and opinion terms.
Aspect-Based Sentiment Analysis
Aspect Sentiment Triplet Extraction
+2
Label-Based Diversity Measure Among Hidden Units of Deep Neural Networks: A Regularization Method
In this paper, from an information theoretic perspective, we introduce a new definition of redundancy to describe the diversity of hidden units under supervised learning settings by formalizing the effect of hidden layers on the generalization capacity as the mutual information.
A Survey of Quantum Theory Inspired Approaches to Information Retrieval
Since 2004, researchers have been using the mathematical framework of Quantum Theory (QT) in Information Retrieval (IR).
End-to-end Emotion-Cause Pair Extraction via Learning to Link
Specifically, our model regards pair extraction as a link prediction task, and learns to link from emotion clauses to cause clauses, i. e., the links are directional.
Ranked #12 on
Emotion-Cause Pair Extraction
on ECPE
Assessing the Memory Ability of Recurrent Neural Networks
To tackle the problem, in this paper, we identify and analyze the internal and external factors that affect the memory ability of RNNs, and propose a Semantic Euclidean Space to represent the semantics expressed by a sequence.
Quantum-like Structure in Multidimensional Relevance Judgements
A large number of studies in cognitive science have revealed that probabilistic outcomes of certain human decisions do not agree with the axioms of classical probability theory.
Leveraging Entanglement Entropy for Deep Understanding of Attention Matrix in Text Matching
We prove that by a mapping (via the trace operator) on the high-dimensional matching matrix, a low-dimensional attention matrix can be derived.
Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution Network
It has been widely accepted that Long Short-Term Memory (LSTM) network, coupled with attention mechanism and memory module, is useful for aspect-level sentiment classification.
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks
Due to their inherent capability in semantic alignment of aspects and their context words, attention mechanism and Convolutional Neural Networks (CNNs) are widely applied for aspect-based sentiment classification.
Modelling Dynamic Interactions between Relevance Dimensions
We study the interaction between the relevance dimensions using the mathematical framework of Quantum Theory.
ScenarioSA: A Large Scale Conversational Database for Interactive Sentiment Analysis
Existing sentiment analysis approaches are insufficient in modelling the interactions among people.
A Tensorized Transformer for Language Modeling
In this paper, based on the ideas of tensor decomposition and parameters sharing, we propose a novel self-attention model (namely Multi-linear attention) with Block-Term Tensor Decomposition (BTD).
Semantic Hilbert Space for Text Representation Learning
To address this issue, we propose a new framework that models different levels of semantic units (e. g. sememe, word, sentence, and semantic abstraction) on a single \textit{Semantic Hilbert Space}, which naturally admits a non-linear semantic composition by means of a complex-valued vector word representation.
A Generalized Language Model in Tensor Space
Theoretically, we prove that such tensor representation is a generalization of the n-gram language model.
Investigating Bell Inequalities for Multidimensional Relevance Judgments in Information Retrieval
Relevance judgment in Information Retrieval is influenced by multiple factors.
A Quantum Many-body Wave Function Inspired Language Modeling Approach
The recently proposed quantum language model (QLM) aimed at a principled approach to modeling term dependency by applying the quantum probability theory.
Reconciling Irrational Human Behavior with AI based Decision Making: A Quantum Probabilistic Approach
There are many examples of human decision making which cannot be modeled by classical probabilistic and logic models, on which the current AI systems are based.
Investigating Order Effects in Multidimensional Relevance Judgment using Query Logs
There is a growing body of research which has investigated relevance judgment in IR being influenced by multiple factors or dimensions.
Quantum-inspired Complex Word Embedding
A challenging task for word embeddings is to capture the emergent meaning or polarity of a combination of individual words.
Search Personalization with Embeddings
Recent research has shown that the performance of search personalization depends on the richness of user profiles which normally represent the user's topical interests.
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines
We then revisit Boltzmann machines (BM) from a model selection perspective and theoretically show that both the fully visible BM (VBM) and the BM with hidden units can be derived from the general binary multivariate distribution using the CIF principle.
Understanding Boltzmann Machine and Deep Learning via A Confident Information First Principle
Typical dimensionality reduction methods focus on directly reducing the number of random variables while retaining maximal variations in the data.
