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Found 28 papers, 10 papers with code
GRAVL-BERT: Graphical Visual-Linguistic Representations for Multimodal Coreference Resolution
Learning from multimodal data has become a popular research topic in recent years.
REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic Entropy
If a LLM's entropy is higher than the asymptotic entropy (i. e., the LLM is more uncertain than it should be), the THF model predicts a high hallucination hazard, which leads to a lower p threshold in REAL sampling.
Mitigating Bias for Question Answering Models by Tracking Bias Influence
Based on the intuition that a model would lean to be more biased if it learns from a biased example, we measure the bias level of a query instance by observing its influence on another instance.
DiNADO: Norm-Disentangled Neurally-Decomposed Oracles for Controlling Language Models
NeurAlly-Decomposed Oracle (NADO) is a powerful approach for controllable generation with large language models.
Unsupervised Melody-to-Lyric Generation
Automatic melody-to-lyric generation is a task in which song lyrics are generated to go with a given melody.
Unsupervised Melody-Guided Lyrics Generation
At inference time, we leverage the crucial alignments between melody and lyrics and compile the given melody into constraints to guide the generation process.
Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning
Dialogue state tracking (DST) is an important step in dialogue management to keep track of users' beliefs.
Context-Situated Pun Generation
In this work, we propose a new task, context-situated pun generation, where a specific context represented by a set of keywords is provided, and the task is to first identify suitable pun words that are appropriate for the context, then generate puns based on the context keywords and the identified pun words.
ExPUNations: Augmenting Puns with Keywords and Explanations
The tasks of humor understanding and generation are challenging and subjective even for humans, requiring commonsense and real-world knowledge to master.
Building Goal-Oriented Dialogue Systems with Situated Visual Context
Most popular goal-oriented dialogue agents are capable of understanding the conversational context.
Style Control for Schema-Guided Natural Language Generation
Our results show that while high style accuracy and semantic correctness are easier to achieve for more lexically-defined styles with conditional training, stylistic control is also achievable for more semantically complex styles using discriminator-based guided decoding methods.
Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE
InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years.
Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems
Finally, we evaluate our system using a typical movie ticket booking task and show that the dialogue simulator is an essential component of the system that leads to over $50\%$ improvement in turn-level action signature prediction accuracy.
Few Shot Dialogue State Tracking using Meta-learning
Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information, etc.
Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems
Our approach includes a novel goal-sampling technique for sampling plausible user goals and a dialog simulation technique that uses heuristic interplay between the user and the system (Alexa), where the user tries to achieve the sampled goal.
Schema-Guided Natural Language Generation
We train different state-of-the-art models for neural natural language generation on this dataset and show that in many cases, including rich schema information allows our models to produce higher quality outputs both in terms of semantics and diversity.
From Machine Reading Comprehension to Dialogue State Tracking: Bridging the Gap
In this paper, we propose using machine reading comprehension (RC) in state tracking from two perspectives: model architectures and datasets.
MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension
Machine Reading Comprehension (MRC) for question answering (QA), which aims to answer a question given the relevant context passages, is an important way to test the ability of intelligence systems to understand human language.
Dialog State Tracking: A Neural Reading Comprehension Approach
In contrast to traditional state tracking methods where the dialog state is often predicted as a distribution over a closed set of all the possible slot values within an ontology, our method uses a simple attention-based neural network to point to the slot values within the conversation.
Ranked #19 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators
Having explicit feedback on the relevance and interestingness of a system response at each turn can be a useful signal for mitigating such issues and improving system quality by selecting responses from different approaches.
Simple Question Answering with Subgraph Ranking and Joint-Scoring
Using this framework as a starting point, we focus on two aspects: improving subgraph selection through a novel ranking method and leveraging the subject--relation dependency by proposing a joint scoring CNN model with a novel loss function that enforces the well-order of scores.
Practical Semantic Parsing for Spoken Language Understanding
Executable semantic parsing is the task of converting natural language utterances into logical forms that can be directly used as queries to get a response.
Flexible and Scalable State Tracking Framework for Goal-Oriented Dialogue Systems
This limits such systems in two different ways: If there is an update in the task domain, the dialogue system usually needs to be updated or completely re-trained.
Parsing Coordination for Spoken Language Understanding
Typical spoken language understanding systems provide narrow semantic parses using a domain-specific ontology.
The Alexa Meaning Representation Language
This paper introduces a meaning representation for spoken language understanding.
