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Found 100 papers, 34 papers with code
Personality Predictive Lexical Cues and Their Correlations
In recent years, a number of studies have used linear models for personality prediction based on text.
Improving Personalized Explanation Generation through Visualization
In modern recommender systems, there are usually comments or reviews from users that justify their ratings for different items.
EmoTag1200: Understanding the Association between Emojis and Emotions
Given the growing ubiquity of emojis in language, there is a need for methods and resources that shed light on their meaning and communicative role.
Multi-Scale Distribution Deep Variational Autoencoder for Explanation Generation
Generating explanations for recommender systems is essential for improving their transparency, as users often wish to understand the reason for receiving a specified recommendation.
Exploiting Image–Text Synergy for Contextual Image Captioning
Modern web content - news articles, blog posts, educational resources, marketing brochures - is predominantly multimodal.
CommitBench: A Benchmark for Commit Message Generation
Writing commit messages is a tedious daily task for many software developers, and often remains neglected.
On the Challenges and Opportunities in Generative AI
The field of deep generative modeling has grown rapidly and consistently over the years.
NextLevelBERT: Investigating Masked Language Modeling with Higher-Level Representations for Long Documents
While (large) language models have significantly improved over the last years, they still struggle to sensibly process long sequences found, e. g., in books, due to the quadratic scaling of the underlying attention mechanism.
MedBench: A Large-Scale Chinese Benchmark for Evaluating Medical Large Language Models
The emergence of various medical large language models (LLMs) in the medical domain has highlighted the need for unified evaluation standards, as manual evaluation of LLMs proves to be time-consuming and labor-intensive.
Efficient Parallelization Layouts for Large-Scale Distributed Model Training
In this work, we conduct a comprehensive ablation study of possible training configurations for large language models.
IntentDial: An Intent Graph based Multi-Turn Dialogue System with Reasoning Path Visualization
Intent detection and identification from multi-turn dialogue has become a widely explored technique in conversational agents, for example, voice assistants and intelligent customer services.
FARSEC: A Reproducible Framework for Automatic Real-Time Vehicle Speed Estimation Using Traffic Cameras
To address this, we provide a novel framework for automatic real-time vehicle speed calculation, which copes with more diverse data from publicly available traffic cameras to achieve greater robustness.
Large Language Models and Knowledge Graphs: Opportunities and Challenges
Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm.
Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification Using Graph Neural Networks?
Given the success of Graph Neural Networks (GNNs) for structure-aware machine learning, many studies have explored their use for text classification, but mostly in specific domains with limited data characteristics.
FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models
However, if we want to use a new tokenizer specialized for the target language, we cannot transfer the source model's embedding matrix.
MultiModal Bias: Introducing a Framework for Stereotypical Bias Assessment beyond Gender and Race in Vision Language Models
Recent breakthroughs in self supervised training have led to a new class of pretrained vision language models.
Democratization of Retail Trading: Can Reddit's WallStreetBets Outperform Investment Bank Analysts?
We present a data-driven empirical study of investment recommendations of WSB in comparison to recommendations made by leading investment banks, based on more than 1. 6 million WSB posts published since 2018.
ViLPAct: A Benchmark for Compositional Generalization on Multimodal Human Activities
We introduce ViLPAct, a novel vision-language benchmark for human activity planning.
Frozen CLIP Models are Efficient Video Learners
Video recognition has been dominated by the end-to-end learning paradigm -- first initializing a video recognition model with weights of a pretrained image model and then conducting end-to-end training on videos.
Ranked #26 on
Action Classification
on Kinetics-400
(using extra training data)
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text Representation
We propose to use a top-down parser as a model-based pruning method, which also enables parallel encoding during inference.
Art Creation with Multi-Conditional StyleGANs
Our approach is based on the StyleGAN neural network architecture, but incorporates a custom multi-conditional control mechanism that provides fine-granular control over characteristics of the generated paintings, e. g., with regard to the perceived emotion evoked in a spectator.
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?
This work evaluates the effectiveness of CLIP for the task of Medical Visual Question Answering (MedVQA).
Ranked #7 on
Medical Visual Question Answering
on SLAKE-English
D-HYPR: Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning
Digraph Representation Learning (DRL) aims to learn representations for directed homogeneous graphs (digraphs).
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
Dense Contrastive Visual-Linguistic Pretraining
To overcome these issues, we propose unbiased Dense Contrastive Visual-Linguistic Pretraining (DCVLP), which replaces the region regression and classification with cross-modality region contrastive learning that requires no annotations.
Data Augmentation with Adversarial Training for Cross-Lingual NLI
Due to recent pretrained multilingual representation models, it has become feasible to exploit labeled data from one language to train a cross-lingual model that can then be applied to multiple new languages.
R2D2: Recursive Transformer based on Differentiable Tree for Interpretable Hierarchical Language Modeling
Human language understanding operates at multiple levels of granularity (e. g., words, phrases, and sentences) with increasing levels of abstraction that can be hierarchically combined.
Should You Take Investment Advice From WallStreetBets? A Data-Driven Approach
200% over the last three years and approx.
Guilt by Association: Emotion Intensities in Lexical Representations
What do word vector representations reveal about the emotions associated with words?
Context-Aware Interaction Network for Question Matching
Impressive milestones have been achieved in text matching by adopting a cross-attention mechanism to capture pertinent semantic connections between two sentence representations.
Faithfully Explainable Recommendation via Neural Logic Reasoning
Knowledge graphs (KG) have become increasingly important to endow modern recommender systems with the ability to generate traceable reasoning paths to explain the recommendation process.
Fast and Effective Biomedical Entity Linking Using a Dual Encoder
Additionally, we modify our dual encoder model for end-to-end biomedical entity linking that performs both mention span detection and entity disambiguation and out-performs two recently proposed models.
Assessing Emoji Use in Modern Text Processing Tools
Emojis have become ubiquitous in digital communication, due to their visual appeal as well as their ability to vividly convey human emotion, among other factors.
Interactive Question Clarification in Dialogue via Reinforcement Learning
In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query.
Sentence Analogies: Linguistic Regularities in Sentence Embeddings
While important properties of word vector representations have been studied extensively, far less is known about the properties of sentence vector representations.
Data Augmentation for Multiclass Utterance Classification -- A Systematic Study
Utterance classification is a key component in many conversational systems.
Cross-Lingual Emotion Lexicon Induction using Representation Alignment in Low-Resource Settings
Emotion lexicons provide information about associations between words and emotions.
Query Distillation: BERT-based Distillation for Ensemble Ranking
We present a novel two-stage distillation method for ranking problems that allows a smaller student model to be trained while benefitting from the better performance of the teacher model, providing better control of the inference latency and computational burden.
Domain-Specific Sentiment Lexicons Induced from Labeled Documents
Sentiment analysis is an area of substantial relevance both in industry and in academia, including for instance in social studies.
Cross-Domain Learning for Classifying Propaganda in Online Contents
Prior work has focused on supervised learning with training data from the same domain.
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
User profiles can capture prominent user behaviors from the history, and provide valuable signals about which kinds of path patterns are more likely to lead to potential items of interest for the user.
GitEvolve: Predicting the Evolution of GitHub Repositories
Given its crucial role, there is a need to better understand and model the dynamics of GitHub as a social platform.
COOKIE: A Dataset for Conversational Recommendation over Knowledge Graphs in E-commerce
In this work, we present a new dataset for conversational recommendation over knowledge graphs in e-commerce platforms called COOKIE.
Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification
The resulting model then serves as a teacher to induce labels for unlabeled target language samples that can be used during further adversarial training, allowing us to gradually adapt our model to the target language.
Contrastive Visual-Linguistic Pretraining
We evaluate CVLP on several down-stream tasks, including VQA, GQA and NLVR2 to validate the superiority of contrastive learning on multi-modality representation learning.
Incorporating Pragmatic Reasoning Communication into Emergent Language
Emergentism and pragmatics are two research fields that study the dynamics of linguistic communication along substantially different timescales and intelligence levels.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
+2
Fairness-Aware Explainable Recommendation over Knowledge Graphs
There has been growing attention on fairness considerations recently, especially in the context of intelligent decision making systems.
TIME: Text and Image Mutual-Translation Adversarial Networks
Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework.
Character Matters: Video Story Understanding with Character-Aware Relations
Without identifying the connection between appearing people and character names, a model is not able to obtain a genuine understanding of the plots.
Correcting the Autocorrect: Context-Aware Typographical Error Correction via Training Data Augmentation
In this paper, we explore the artificial generation of typographical errors based on real-world statistics.
Are Emojis Emotional? A Study to Understand the Association between Emojis and Emotions
Given the growing ubiquity of emojis in language, there is a need for methods and resources that shed light on their meaning and communicative role.
Explainable Link Prediction for Emerging Entities in Knowledge Graphs
Despite their large-scale coverage, cross-domain knowledge graphs invariably suffer from inherent incompleteness and sparsity.
Inducing Universal Semantic Tag Vectors
Given the well-established usefulness of part-of-speech tag annotations in many syntactically oriented downstream NLP tasks, the recently proposed notion of semantic tagging (Bjerva et al. 2016) aims at tagging words with tags informed by semantic distinctions, which are likely to be useful across a range of semantic tasks.
HID: Hierarchical Multiscale Representation Learning for Information Diffusion
In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling.
Sentence Analogies: Exploring Linguistic Relationships and Regularities in Sentence Embeddings
While important properties of word vector representations have been studied extensively, far less is known about the properties of sentence vector representations.
Knowledge Graphs
In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data.
Long Short-Term Sample Distillation
The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher--student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates.
ABSent: Cross-Lingual Sentence Representation Mapping with Bidirectional GANs
A number of cross-lingual transfer learning approaches based on neural networks have been proposed for the case when large amounts of parallel text are at our disposal.
Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation
In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous.
A Robust Self-Learning Framework for Cross-Lingual Text Classification
Based on massive amounts of data, recent pretrained contextual representation models have made significant strides in advancing a number of different English NLP tasks.
SCALOR: Generative World Models with Scalable Object Representations
Scalability in terms of object density in a scene is a primary challenge in unsupervised sequential object-oriented representation learning.
EmoTag -- Towards an Emotion-Based Analysis of Emojis
We evaluate the induced emotion profiles of emojis with regard to their ability to predict word affect intensities as well as sentiment scores.
Rhetorically Controlled Encoder-Decoder for Modern Chinese Poetry Generation
Rhetoric is a vital element in modern poetry, and plays an essential role in improving its aesthetics.
Reinforcement Knowledge Graph Reasoning for Explainable Recommendation
To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph.
OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization
Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regularization (OOGAN).
Be Concise and Precise: Synthesizing Open-Domain Entity Descriptions from Facts
Despite being vast repositories of factual information, cross-domain knowledge graphs, such as Wikidata and the Google Knowledge Graph, only sparsely provide short synoptic descriptions for entities.
CITE: A Corpus of Image-Text Discourse Relations
This paper presents a novel crowd-sourced resource for multimodal discourse: our resource characterizes inferences in image-text contexts in the domain of cooking recipes in the form of coherence relations.
Using Multi-Sense Vector Embeddings for Reverse Dictionaries
However, they typically cannot serve as a drop-in replacement for conventional single-sense embeddings, because the correct sense vector needs to be selected for each word.
A Helping Hand: Transfer Learning for Deep Sentiment Analysis
Deep convolutional neural networks excel at sentiment polarity classification, but tend to require substantial amounts of training data, which moreover differs quite significantly between domains.
Ranked #74 on
Sentiment Analysis
on SST-2 Binary classification
Exploring Semantic Properties of Sentence Embeddings
Neural vector representations are ubiquitous throughout all subfields of NLP.
Generating Fine-Grained Open Vocabulary Entity Type Descriptions
While large-scale knowledge graphs provide vast amounts of structured facts about entities, a short textual description can often be useful to succinctly characterize an entity and its type.
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
In this paper, however, we show that temporal information, especially longer-term patterns, may not be necessary to achieve competitive results on common video classification datasets.
Multilingual Vector Representations of Words, Sentences, and Documents
Neural vector representations are now ubiquitous in all subfields of natural language processing and text mining.
Co-PACRR: A Context-Aware Neural IR Model for Ad-hoc Retrieval
Neural IR models, such as DRMM and PACRR, have achieved strong results by successfully capturing relevance matching signals.
PACRR: A Position-Aware Neural IR Model for Relevance Matching
In order to adopt deep learning for information retrieval, models are needed that can capture all relevant information required to assess the relevance of a document to a given user query.
Video Captioning with Multi-Faceted Attention
Recently, video captioning has been attracting an increasing amount of interest, due to its potential for improving accessibility and information retrieval.
Should Algorithms for Random SAT and Max-SAT be Different?
We analyze to what extent the random SAT and Max-SAT problems differ in their properties.
The Open Linguistics Working Group: Developing the Linguistic Linked Open Data Cloud
The Open Linguistics Working Group (OWLG) brings together researchers from various fields of linguistics, natural language processing, and information technology to present and discuss principles, case studies, and best practices for representing, publishing and linking linguistic data collections.
Medical Concept Embeddings via Labeled Background Corpora
In recent years, we have seen an increasing amount of interest in low-dimensional vector representations of words.
Detecting Cross-Cultural Differences Using a Multilingual Topic Model
Understanding cross-cultural differences has important implications for world affairs and many aspects of the life of society.
NomLex-PT: A Lexicon of Portuguese Nominalizations
This paper presents NomLex-PT, a lexical resource describing Portuguese nominalizations.
Etymological Wordnet: Tracing The History of Words
Research on the history of words has led to remarkable insights about language and also about the history of human civilization more generally.
Bring vs. MTRoget: Evaluating automatic thesaurus translation
Evaluation of automatic language-independent methods for language technology resource creation is difficult, and confounded by a largely unknown quantity, viz.
Good, Great, Excellent: Global Inference of Semantic Intensities
Adjectives like good, great, and excellent are similar in meaning, but differ in intensity.
Empirical Comparisons of MASC Word Sense Annotations
We analyze how different conceptions of lexical semantics affect sense annotations and how multiple sense inventories can be compared empirically, based on annotated text.
