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Found 68 papers, 38 papers with code
Question Dependent Recurrent Entity Network for Question Answering
Question Answering is a task which requires building models capable of providing answers to questions expressed in human language.
FA3L at SemEval-2017 Task 3: A ThRee Embeddings Recurrent Neural Network for Question Answering
In this paper we present ThReeNN, a model for Community Question Answering, Task 3, of SemEval-2017.
Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems
End-to-end task-oriented dialog systems usually suffer from the challenge of incorporating knowledge bases.
Ranked #10 on
Task-Oriented Dialogue Systems
on KVRET
Bilingual Character Representation for Efficiently Addressing Out-of-Vocabulary Words in Code-Switching Named Entity Recognition
We propose an LSTM-based model with hierarchical architecture on named entity recognition from code-switching Twitter data.
Code-Switching Language Modeling using Syntax-Aware Multi-Task Learning
Lack of text data has been the major issue on code-switching language modeling.
Emo2Vec: Learning Generalized Emotion Representation by Multi-task Training
In this paper, we propose Emo2Vec which encodes emotional semantics into vectors.
Ranked #28 on
Sentiment Analysis
on SST-5 Fine-grained classification
Learn to Code-Switch: Data Augmentation using Copy Mechanism on Language Modeling
Building large-scale datasets for training code-switching language models is challenging and very expensive.
Towards End-to-end Automatic Code-Switching Speech Recognition
Speech recognition in mixed language has difficulties to adapt end-to-end framework due to the lack of data and overlapping phone sets, for example in words such as "one" in English and "w\`an" in Chinese.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Transferable Multi-Domain State Generator for Task-Oriented Dialogue Systems
Over-dependence on domain ontology and lack of knowledge sharing across domains are two practical and yet less studied problems of dialogue state tracking.
Ranked #15 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+2
Personalizing Dialogue Agents via Meta-Learning
Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency.
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification
Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed.
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification
Detecting emotion from dialogue is a challenge that has not yet been extensively surveyed.
Generating Empathetic Responses by Looking Ahead the User's Sentiment
Hence, in this paper, we propose Sentiment Look-ahead, which is a novel perspective for empathy that models the future user emotional state.
Exploring Social Bias in Chatbots using Stereotype Knowledge
Exploring social bias in chatbot is an important, yet relatively unexplored problem.
Getting To Know You: User Attribute Extraction from Dialogues
User attributes provide rich and useful information for user understanding, yet structured and easy-to-use attributes are often sparsely populated.
MoEL: Mixture of Empathetic Listeners
Previous research on empathetic dialogue systems has mostly focused on generating responses given certain emotions.
On the Effectiveness of Low-Rank Matrix Factorization for LSTM Model Compression
Despite their ubiquity in NLP tasks, Long Short-Term Memory (LSTM) networks suffer from computational inefficiencies caused by inherent unparallelizable recurrences, which further aggravates as LSTMs require more parameters for larger memory capacity.
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning
Sensational headlines are headlines that capture people's attention and generate reader interest.
Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences
Training code-switched language models is difficult due to lack of data and complexity in the grammatical structure.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Zero-shot Cross-lingual Dialogue Systems with Transferable Latent Variables
Despite the surging demands for multilingual task-oriented dialog systems (e. g., Alexa, Google Home), there has been less research done in multilingual or cross-lingual scenarios.
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities.
Attention over Parameters for Dialogue Systems
Dialogue systems require a great deal of different but complementary expertise to assist, inform, and entertain humans.
Exploration Based Language Learning for Text-Based Games
In this work, we propose to use the exploration approach of Go-Explore for solving text-based games.
On the Importance of Word Order Information in Cross-lingual Sequence Labeling
To verify this hypothesis, we investigate whether making models insensitive to the word order of the source language can improve the adaptation performance in target languages.
Learning Fast Adaptation on Cross-Accented Speech Recognition
The great variability and complex characteristics of accents creates a major challenge for training a robust and accent-agnostic automatic speech recognition (ASR) system.
Audio and Speech Processing Sound
XPersona: Evaluating Multilingual Personalized Chatbot
Experimental results show that the multilingual trained models outperform the translation-pipeline and that they are on par with the monolingual models, with the advantage of having a single model across multiple languages.
Exploring Versatile Generative Language Model Via Parameter-Efficient Transfer Learning
Fine-tuning pre-trained generative language models to down-stream language generation tasks has shown promising results.
Exploring Fine-tuning Techniques for Pre-trained Cross-lingual Models via Continual Learning
Recently, fine-tuning pre-trained language models (e. g., multilingual BERT) to downstream cross-lingual tasks has shown promising results.
Misinformation Has High Perplexity
Debunking misinformation is an important and time-critical task as there could be adverse consequences when misinformation is not quashed promptly.
Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems
In this paper, we evaluate the priming few-shot ability of language models in the NLU, DST, DP and NLG tasks.
The Adapter-Bot: All-In-One Controllable Conversational Model
The dialogue skills can be triggered automatically via a dialogue manager, or manually, thus allowing high-level control of the generated responses.
MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems
In this paper, we propose Minimalist Transfer Learning (MinTL) to simplify the system design process of task-oriented dialogue systems and alleviate the over-dependency on annotated data.
Ranked #15 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+3
Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems
In this paper, we propose a method to embed the KB, of any size, directly into the model parameters.
Plug-and-Play Conversational Models
These large conversational models provide little control over the generated responses, and this control is further limited in the absence of annotated conversational datasets for attribute specific generation that can be used for fine-tuning the model.
CrossNER: Evaluating Cross-Domain Named Entity Recognition
Cross-domain named entity recognition (NER) models are able to cope with the scarcity issue of NER samples in target domains.
Continual Learning in Task-Oriented Dialogue Systems
Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining.
Towards Few-Shot Fact-Checking via Perplexity
Through experiments, we empirically verify the plausibility of the rather surprising usage of the perplexity score in the context of fact-checking and highlight the strength of our few-shot methodology by comparing it to strong fine-tuning-based baseline models.
Are Multilingual Models Effective in Code-Switching?
Multilingual language models have shown decent performance in multilingual and cross-lingual natural language understanding tasks.
Mitigating Media Bias through Neutral Article Generation
Media bias can lead to increased political polarization, and thus, the need for automatic mitigation methods is growing.
Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding
Dialogue systems powered by large pre-trained language models (LM) exhibit an innate ability to deliver fluent and natural-looking responses.
Dynamically Addressing Unseen Rumor via Continual Learning
Rumors are often associated with newly emerging events, thus, an ability to deal with unseen rumors is crucial for a rumor veracity classification model.
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue State Tracking
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data.
Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters
This paper proposes KnowExpert, a framework to bypass the explicit retrieval process and inject knowledge into the pre-trained language models with lightweight adapters and adapt to the knowledge-grounded dialogue task.
QAConv: Question Answering on Informative Conversations
This paper introduces QAConv, a new question answering (QA) dataset that uses conversations as a knowledge source.
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTracking
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle unseen domains without the expense of collecting in-domain data.
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue Modeling
However, existing datasets for end-to-end ToD modeling are limited to a single language, hindering the development of robust end-to-end ToD systems for multilingual countries and regions.
CAiRE in DialDoc21: Data Augmentation for Information-Seeking Dialogue System
Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users' needs, which.
Assessing Political Prudence of Open-domain Chatbots
In this work, as a first step towards a politically safe chatbot, we propose a group of metrics for assessing their political prudence.
Taming the Beast: Learning to Control Neural Conversational Models
However, these systems cannot be easily controlled and extended as the modularized dialogue manager can.
Zero-Shot Dialogue State Tracking via Cross-Task Transfer
Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data.
Language Models are Few-shot Multilingual Learners
General-purpose language models have demonstrated impressive capabilities, performing on par with state-of-the-art approaches on a range of downstream natural language processing (NLP) tasks and benchmarks when inferring instructions from very few examples.
Few-Shot Bot: Prompt-Based Learning for Dialogue Systems
A simple yet unexplored solution is prompt-based few-shot learning (Brown et al. 2020) which does not require gradient-based fine-tuning but instead uses a few examples in the LM context as the only source of learning.
Survey of Hallucination in Natural Language Generation
This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation.
VScript: Controllable Script Generation with Visual Presentation
In order to offer a customized script tool and inspire professional scriptwriters, we present VScript.
Dialogue Summaries as Dialogue States (DS2), Template-Guided Summarization for Few-shot Dialogue State Tracking
In this paper, we hypothesize that dialogue summaries are essentially unstructured dialogue states; hence, we propose to reformulate dialogue state tracking as a dialogue summarization problem.
Abstractive Dialogue Summarization
Dialogue State Tracking
+2
NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias
Based on our discovery that title provides a good signal for framing bias, we present NeuS-TITLE that learns to neutralize news content in hierarchical order from title to article.
Towards Answering Open-ended Ethical Quandary Questions
We explore the current capability of LLMs in providing an answer with a deliberative exchange of different perspectives to an ethical quandary, in the approach of Socratic philosophy, instead of providing a closed answer like an oracle.
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.
Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values
Therefore, we introduce a framework for value-aligned classification that performs prediction based on explicitly written human values in the command.
IMU2CLIP: Multimodal Contrastive Learning for IMU Motion Sensors from Egocentric Videos and Text
We present IMU2CLIP, a novel pre-training approach to align Inertial Measurement Unit (IMU) motion sensor recordings with video and text, by projecting them into the joint representation space of Contrastive Language-Image Pre-training (CLIP).
Continual Dialogue State Tracking via Example-Guided Question Answering
Dialogue systems are frequently updated to accommodate new services, but naively updating them by continually training with data for new services in diminishing performance on previously learnt services.
Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts
Many cognitive approaches to well-being, such as recognizing and reframing unhelpful thoughts, have received considerable empirical support over the past decades, yet still lack truly widespread adoption in self-help format.
AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model
We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals (i. e. text, image, video, audio, IMU motion sensor), and generates textual responses.
Ranked #7 on
Video Question Answering
on STAR Benchmark
Fine-Tuned Language Models Generate Stable Inorganic Materials as Text
We propose fine-tuning large language models for generation of stable materials.
SnapNTell: Enhancing Entity-Centric Visual Question Answering with Retrieval Augmented Multimodal LLM
We have developed the \textbf{SnapNTell Dataset}, distinct from traditional VQA datasets: (1) It encompasses a wide range of categorized entities, each represented by images and explicitly named in the answers; (2) It features QA pairs that require extensive knowledge for accurate responses.
Preserving Cross-Linguality of Pre-trained Models via Continual Learning
Recently, fine-tuning pre-trained language models (e. g., multilingual BERT) to downstream cross-lingual tasks has shown promising results.
CAiRE in DialDoc21: Data Augmentation for Information Seeking Dialogue System
Information-seeking dialogue systems, including knowledge identification and response generation, aim to respond to users with fluent, coherent, and informative responses based on users’ needs, which.
