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Found 68 papers, 28 papers with code
Leveraging Twitter for Low-Resource Conversational Speech Language Modeling
In applications involving conversational speech, data sparsity is a limiting factor in building a better language model.
What Your Username Says About You
Experimental results on the two tasks demonstrate the effectiveness of the proposed morphological features compared to a character n-gram baseline.
Talking to the crowd: What do people react to in online discussions?
This paper addresses the question of how language use affects community reaction to comments in online discussion forums, and the relative importance of the message vs. the messenger.
Deep Reinforcement Learning with a Natural Language Action Space
This paper introduces a novel architecture for reinforcement learning with deep neural networks designed to handle state and action spaces characterized by natural language, as found in text-based games.
LSTM based Conversation Models
In this paper, we present a conversational model that incorporates both context and participant role for two-party conversations.
Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding
The goal of this paper is to use multi-task learning to efficiently scale slot filling models for natural language understanding to handle multiple target tasks or domains.
Disfluency Detection using a Bidirectional LSTM
We introduce a new approach for disfluency detection using a Bidirectional Long-Short Term Memory neural network (BLSTM).
Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space.
Hierarchical Character-Word Models for Language Identification
Social media messages' brevity and unconventional spelling pose a challenge to language identification.
Learning Latent Local Conversation Modes for Predicting Community Endorsement in Online Discussions
Many social media platforms offer a mechanism for readers to react to comments, both positively and negatively, which in aggregate can be thought of as community endorsement.
Characterizing the Language of Online Communities and its Relation to Community Reception
This work investigates style and topic aspects of language in online communities: looking at both utility as an identifier of the community and correlation with community reception of content.
Conversation Modeling on Reddit using a Graph-Structured LSTM
This paper presents a novel approach for modeling threaded discussions on social media using a graph-structured bidirectional LSTM which represents both hierarchical and temporal conversation structure.
Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads
This paper addresses the problem of predicting popularity of comments in an online discussion forum using reinforcement learning, particularly addressing two challenges that arise from having natural language state and action spaces.
Improving Context Aware Language Models
Increased adaptability of RNN language models leads to improved predictions that benefit many applications.
Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information
In conversational speech, the acoustic signal provides cues that help listeners disambiguate difficult parses.
Scientific Information Extraction with Semi-supervised Neural Tagging
This paper addresses the problem of extracting keyphrases from scientific articles and categorizing them as corresponding to a task, process, or material.
A Factored Neural Network Model for Characterizing Online Discussions in Vector Space
We develop a novel factored neural model that learns comment embeddings in an unsupervised way leveraging the structure of distributional context in online discussion forums.
Language Based Mapping of Science Assessment Items to Skills
Knowledge of the association between assessment questions and the skills required to solve them is necessary for analysis of student learning.
Low-Rank RNN Adaptation for Context-Aware Language Modeling
A context-aware language model uses location, user and/or domain metadata (context) to adapt its predictions.
Real-Time Prediction of the Duration of Distribution System Outages
This paper addresses the problem of predicting duration of unplanned power outages, using historical outage records to train a series of neural network predictors.
Community Member Retrieval on Social Media using Textual Information
This paper addresses the problem of community membership detection using only text features in a scenario where a small number of positive labeled examples defines the community.
Personalized Language Model for Query Auto-Completion
Query auto-completion is a search engine feature whereby the system suggests completed queries as the user types.
Sounding Board: A User-Centric and Content-Driven Social Chatbot
We present Sounding Board, a social chatbot that won the 2017 Amazon Alexa Prize.
Estimating Linguistic Complexity for Science Texts
Evaluation of text difficulty is important both for downstream tasks like text simplification, and for supporting educators in classrooms.
The UWNLP system at SemEval-2018 Task 7: Neural Relation Extraction Model with Selectively Incorporated Concept Embeddings
This paper describes our submission for SemEval 2018 Task 7 shared task on semantic relation extraction and classification in scientific papers.
Domain Adversarial Training for Accented Speech Recognition
In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem.
Training Augmentation with Adversarial Examples for Robust Speech Recognition
This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models.
Scientific Relation Extraction with Selectively Incorporated Concept Embeddings
This paper describes our submission for the SemEval 2018 Task 7 shared task on semantic relation extraction and classification in scientific papers.
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
We introduce a multi-task setup of identifying and classifying entities, relations, and coreference clusters in scientific articles.
Ranked #7 on
Named Entity Recognition (NER)
on SciERC
Coreference Resolution
Joint Entity and Relation Extraction
+2
Robust cross-domain disfluency detection with pattern match networks
In this paper we introduce a novel pattern match neural network architecture that uses neighbor similarity scores as features, eliminating the need for feature engineering in a disfluency detection task.
A General Framework for Information Extraction using Dynamic Span Graphs
We introduce a general framework for several information extraction tasks that share span representations using dynamically constructed span graphs.
Ranked #1 on
Relation Extraction
on ACE 2004
(Cross Sentence metric)
Joint Entity and Relation Extraction
Named Entity Recognition (NER)
+1
Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection
Disfluencies in spontaneous speech are known to be associated with prosodic disruptions.
Disfluencies and Human Speech Transcription Errors
This paper explores contexts associated with errors in transcrip-tion of spontaneous speech, shedding light on human perceptionof disfluencies and other conversational speech phenomena.
A Dynamic Speaker Model for Conversational Interactions
Characterizing these differences can be useful in human-computer interaction, as well as analysis of human-human conversations.
Automated Essay Scoring with Discourse-Aware Neural Models
Automated essay scoring systems typically rely on hand-crafted features to predict essay quality, but such systems are limited by the cost of feature engineering.
Annotating Social Determinants of Health Using Active Learning, and Characterizing Determinants Using Neural Event Extraction
The Social History Annotation Corpus (SHAC) includes 4, 480 social history sections with detailed annotation for 12 SDOH characterizing the status, extent, and temporal information of 18K distinct events.
A Controllable Model of Grounded Response Generation
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting responses.
Extracting Summary Knowledge Graphs from Long Documents
Knowledge graphs capture entities and relations from long documents and can facilitate reasoning in many downstream applications.
Analysis of Disfluency in Children's Speech
Disfluencies are prevalent in spontaneous speech, as shown in many studies of adult speech.
On the Role of Style in Parsing Speech with Neural Models
The differences in written text and conversational speech are substantial; previous parsers trained on treebanked text have given very poor results on spontaneous speech.
Extracting COVID-19 Diagnoses and Symptoms From Clinical Text: A New Annotated Corpus and Neural Event Extraction Framework
In a secondary use application, we explored the prediction of COVID-19 test results using structured patient data (e. g. vital signs and laboratory results) and automatically extracted symptom information.
Representations for Question Answering from Documents with Tables and Text
Tables in Web documents are pervasive and can be directly used to answer many of the queries searched on the Web, motivating their integration in question answering.
Assessing the Use of Prosody in Constituency Parsing of Imperfect Transcripts
This work explores constituency parsing on automatically recognized transcripts of conversational speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
DIALKI: Knowledge Identification in Conversational Systems through Dialogue-Document Contextualization
Identifying relevant knowledge to be used in conversational systems that are grounded in long documents is critical to effective response generation.
Dialogue State Tracking with a Language Model using Schema-Driven Prompting
Task-oriented conversational systems often use dialogue state tracking to represent the user's intentions, which involves filling in values of pre-defined slots.
Ranked #1 on
Dialogue State Tracking
on MULTIWOZ 2.1
(MultiWOZ (Joint Goal Acc) metric)
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning
Compared to standard retrieval tasks, passage retrieval for conversational question answering (CQA) poses new challenges in understanding the current user question, as each question needs to be interpreted within the dialogue context.
In-Context Learning for Few-Shot Dialogue State Tracking
In this work, we propose an in-context learning (ICL) framework for zero-shot and few-shot learning DST, where a large pre-trained language model (LM) takes a test instance and a few exemplars as input, and directly decodes the dialogue state without any parameter updates.
Automatic Dialect Density Estimation for African American English
In this paper, we explore automatic prediction of dialect density of the African American English (AAE) dialect, where dialect density is defined as the percentage of words in an utterance that contain characteristics of the non-standard dialect.
Unsupervised Learning of Hierarchical Conversation Structure
Human conversations can evolve in many different ways, creating challenges for automatic understanding and summarization.
INSCIT: Information-Seeking Conversations with Mixed-Initiative Interactions
In an information-seeking conversation, a user may ask questions that are under-specified or unanswerable.
Selective Annotation Makes Language Models Better Few-Shot Learners
Departing from recent in-context learning methods, we formulate an annotation-efficient, two-step framework: selective annotation that chooses a pool of examples to annotate from unlabeled data in advance, followed by prompt retrieval that retrieves task examples from the annotated pool at test time.
Generalizing through Forgetting -- Domain Generalization for Symptom Event Extraction in Clinical Notes
To reduce reliance on domain-specific features, we propose a domain generalization method that dynamically masks frequent symptoms words in the source domain.
Binding Language Models in Symbolic Languages
We propose Binder, a training-free neural-symbolic framework that maps the task input to a program, which (1) allows binding a unified API of language model (LM) functionalities to a programming language (e. g., SQL, Python) to extend its grammar coverage and thus tackle more diverse questions, (2) adopts an LM as both the program parser and the underlying model called by the API during execution, and (3) requires only a few in-context exemplar annotations.
Ranked #4 on
Table-based Fact Verification
on TabFact
One Embedder, Any Task: Instruction-Finetuned Text Embeddings
Our analysis suggests that INSTRUCTOR is robust to changes in instructions, and that instruction finetuning mitigates the challenge of training a single model on diverse datasets.
TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answering
We introduce TIFA (Text-to-Image Faithfulness evaluation with question Answering), an automatic evaluation metric that measures the faithfulness of a generated image to its text input via visual question answering (VQA).
Fine-Grained Human Feedback Gives Better Rewards for Language Model Training
We introduce Fine-Grained RLHF, a framework that enables training and learning from reward functions that are fine-grained in two respects: (1) density, providing a reward after every segment (e. g., a sentence) is generated; and (2) incorporating multiple reward models associated with different feedback types (e. g., factual incorrectness, irrelevance, and information incompleteness).
Building blocks for complex tasks: Robust generative event extraction for radiology reports under domain shifts
This paper explores methods for extracting information from radiology reports that generalize across exam modalities to reduce requirements for annotated data.
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?
The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fact that most data is protected from public release for privacy reasons.
OrchestraLLM: Efficient Orchestration of Language Models for Dialogue State Tracking
Large language models (LLMs) have revolutionized the landscape of Natural Language Processing systems, but are computationally expensive.
Encode Once and Decode in Parallel: Efficient Transformer Decoding
Transformer-based NLP models are powerful but have high computational costs that limit deployment scenarios.
