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Found 53 papers, 25 papers with code
End-to-End Chinese Speaker Identification
End-to-end SI systems, on the other hand, are not limited by individual modules, but suffer from insufficient training data from the existing small-scale datasets.
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference.
Document-Level Machine Translation with Large Language Models
Large language models (LLMs) such as Chat-GPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks.
ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion
However, there has been limited research on the zero-shot KBC settings, where we need to deal with unseen entities and relations that emerge in a constantly growing knowledge base.
NarraSum: A Large-Scale Dataset for Abstractive Narrative Summarization
To encourage research in this direction, we propose NarraSum, a large-scale narrative summarization dataset.
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions.
Knowledge-in-Context: Towards Knowledgeable Semi-Parametric Language Models
In this paper, we develop a novel semi-parametric language model architecture, Knowledge-in-Context (KiC), which empowers a parametric text-to-text language model with a knowledge-rich external memory.
Knowledge-grounded Dialog State Tracking
Knowledge (including structured knowledge such as schema and ontology, and unstructured knowledge such as web corpus) is a critical part of dialog understanding, especially for unseen tasks and domains.
Cross-Lingual Speaker Identification Using Distant Supervision
Speaker identification, determining which character said each utterance in literary text, benefits many downstream tasks.
ReAct: Synergizing Reasoning and Acting in Language Models
While large language models (LLMs) have demonstrated impressive capabilities across tasks in language understanding and interactive decision making, their abilities for reasoning (e. g. chain-of-thought prompting) and acting (e. g. action plan generation) have primarily been studied as separate topics.
Zemi: Learning Zero-Shot Semi-Parametric Language Models from Multiple Tasks
Notably, our proposed $\text{Zemi}_\text{LARGE}$ outperforms T0-3B by 16% on all seven evaluation tasks while being 3. 9x smaller in model size.
Using Chatbots to Teach Languages
This paper reports on progress towards building an online language learning tool to provide learners with conversational experience by using dialog systems as conversation practice partners.
Price Interpretability of Prediction Markets: A Convergence Analysis
Prediction markets are long known for prediction accuracy.
Unsupervised Slot Schema Induction for Task-oriented Dialog
Carefully-designed schemas describing how to collect and annotate dialog corpora are a prerequisite towards building task-oriented dialog systems.
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension
Comprehending a dialogue requires a model to capture diverse kinds of key information in the utterances, which are either scattered around or implicitly implied in different turns of conversations.
C-MORE: Pretraining to Answer Open-Domain Questions by Consulting Millions of References
And with our pretrained reader, the entire system improves by up to 4% in exact match.
Description-Driven Task-Oriented Dialog Modeling
Task-oriented dialogue (TOD) systems are required to identify key information from conversations for the completion of given tasks.
Connect-the-Dots: Bridging Semantics between Words and Definitions via Aligning Word Sense Inventories
We then train a model to identify semantic equivalence between a target word in context and one of its glosses using these aligned inventories, which exhibits strong transfer capability to many WSD tasks.
Automatically Exposing Problems with Neural Dialog Models
Neural dialog models are known to suffer from problems such as generating unsafe and inconsistent responses.
QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining
To address this problem, we introduce QA-driven slot filling (QASF), which extracts slot-filler spans from utterances with a span-based QA model.
Language Embeddings for Typology and Cross-lingual Transfer Learning
Cross-lingual language tasks typically require a substantial amount of annotated data or parallel translation data.
Cross-Lingual Natural Language Inference
Cross-Lingual Transfer
+4
Few-shot Intent Classification and Slot Filling with Retrieved Examples
Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain.
MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations
To validate our scheme, we leveraged transfer learning methods to train a multi-label dialog act prediction model and reached an F1 score of 0. 79.
Attribute Alignment: Controlling Text Generation from Pre-trained Language Models
Large language models benefit from training with a large amount of unlabeled text, which gives them increasingly fluent and diverse generation capabilities.
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data
In spite of much recent research in the area, it is still unclear whether subject-area question-answering data is useful for machine reading comprehension (MRC) tasks.
Gunrock 2.0: A User Adaptive Social Conversational System
Gunrock 2. 0 is built on top of Gunrock with an emphasis on user adaptation.
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge
In this paper, we aim to extract commonsense knowledge to improve machine reading comprehension.
A Efficient Multimodal Framework for Large Scale Emotion Recognition by Fusing Music and Electrodermal Activity Signals
In this paper, our work makes an attempt to fuse the subject individual EDA features and the external evoked music features.
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension
In this paper, we study machine reading comprehension (MRC) on long texts, where a model takes as inputs a lengthy document and a question and then extracts a text span from the document as an answer.
Dialogue-Based Relation Extraction
We present the first human-annotated dialogue-based relation extraction (RE) dataset DialogRE, aiming to support the prediction of relation(s) between two arguments that appear in a dialogue.
Ranked #6 on
Dialog Relation Extraction
on DialogRE
(F1c (v1) metric)
CLUE: A Chinese Language Understanding Evaluation Benchmark
The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks.
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.
Filling Conversation Ellipsis for Better Social Dialog Understanding
We then train a prediction model using both utterances containing ellipsis and our automatically completed utterances.
Improving Pre-Trained Multilingual Model with Vocabulary Expansion
However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language model over large-scale corpora for each language.
Gunrock: A Social Bot for Complex and Engaging Long Conversations
Gunrock is the winner of the 2018 Amazon Alexa Prize, as evaluated by coherence and engagement from both real users and Amazon-selected expert conversationalists.
Improving Pre-Trained Multilingual Models with Vocabulary Expansion
However, in multilingual setting, it is extremely resource-consuming to pre-train a deep language model over large-scale corpora for each language.
Teaching Pretrained Models with Commonsense Reasoning: A Preliminary KB-Based Approach
Recently, pretrained language models (e. g., BERT) have achieved great success on many downstream natural language understanding tasks and exhibit a certain level of commonsense reasoning ability.
Dependency Parsing for Spoken Dialog Systems
Dependency parsing of conversational input can play an important role in language understanding for dialog systems by identifying the relationships between entities extracted from user utterances.
MIDAS: A Dialog Act Annotation Scheme for Open Domain Human Machine Spoken Conversations
Previous dialog act schemes, such as SWBD-DAMSL, are designed for human-human conversations, in which conversation partners have perfect language understanding ability.
User independent Emotion Recognition with Residual Signal-Image Network
To the best of author's knowledge, our method is the first attempt to classify large scale subject-independent emotion with 7962 pieces of EDA signals from 457 subjects.
UC Davis at SemEval-2019 Task 1: DAG Semantic Parsing with Attention-based Decoder
We present an encoder-decoder model for semantic parsing with UCCA SemEval 2019 Task 1.
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension
Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document.
DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension
We present DREAM, the first dialogue-based multiple-choice reading comprehension data set.
Evidence Sentence Extraction for Machine Reading Comprehension
Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks.
Improving Question Answering with External Knowledge
We focus on multiple-choice question answering (QA) tasks in subject areas such as science, where we require both broad background knowledge and the facts from the given subject-area reference corpus.
DREAM: A Challenge Dataset and Models for Dialogue-Based Reading Comprehension
DREAM is likely to present significant challenges for existing reading comprehension systems: 84% of answers are non-extractive, 85% of questions require reasoning beyond a single sentence, and 34% of questions also involve commonsense knowledge.
Improving Machine Reading Comprehension with General Reading Strategies
Reading strategies have been shown to improve comprehension levels, especially for readers lacking adequate prior knowledge.
Ranked #2 on
Question Answering
on Story Cloze
Open Relation Extraction and Grounding
Previous open Relation Extraction (open RE) approaches mainly rely on linguistic patterns and constraints to extract important relational triples from large-scale corpora.
