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Found 44 papers, 14 papers with code
How Self-Attention Improves Rare Class Performance in a Question-Answering Dialogue Agent
Contextualized language modeling using deep Transformer networks has been applied to a variety of natural language processing tasks with remarkable success.
A Multi-Aspect Framework for Counter Narrative Evaluation using Large Language Models
Counter narratives - informed responses to hate speech contexts designed to refute hateful claims and de-escalate encounters - have emerged as an effective hate speech intervention strategy.
End-to-End real time tracking of children's reading with pointer network
To train this pointer network, we generate ground truth training signals by using forced alignment between the read speech and the text being read on the training set.
Selective Demonstrations for Cross-domain Text-to-SQL
Large language models (LLMs) with in-context learning have demonstrated impressive generalization capabilities in the cross-domain text-to-SQL task, without the use of in-domain annotations.
How to Prompt LLMs for Text-to-SQL: A Study in Zero-shot, Single-domain, and Cross-domain Settings
Large language models (LLMs) with in-context learning have demonstrated remarkable capability in the text-to-SQL task.
MapQA: A Dataset for Question Answering on Choropleth Maps
Our experimental results show that V-MODEQA has better overall performance and robustness on MapQA than the state-of-the-art ChartQA and VQA algorithms by capturing the unique properties in map question answering.
Building an ASR Error Robust Spoken Virtual Patient System in a Highly Class-Imbalanced Scenario Without Speech Data
As it is difficult to collect spoken data from users without a functioning SLU system, our method does not rely on spoken data for training, rather we use an ASR error predictor to "speechify" the text data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Tokenwise Contrastive Pretraining for Finer Speech-to-BERT Alignment in End-to-End Speech-to-Intent Systems
Recent advances in End-to-End (E2E) Spoken Language Understanding (SLU) have been primarily due to effective pretraining of speech representations.
Towards End-to-End Integration of Dialog History for Improved Spoken Language Understanding
In the absence of gold transcripts to fine-tune an ASR model, our model outperforms this baseline by a significant margin of 10% absolute F1 score.
Learning Latent Structures for Cross Action Phrase Relations in Wet Lab Protocols
We propose a new model that incrementally learns latent structures and is better suited to resolving inter-sentence relations and implicit arguments.
Hallucination of speech recognition errors with sequence to sequence learning
Automatic Speech Recognition (ASR) is an imperfect process that results in certain mismatches in ASR output text when compared to plain written text or transcriptions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora
Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another.
Contextualized Embeddings for Enriching Linguistic Analyses on Politeness
In this work, we introduce a model which leverages the pre-trained BERT model to cluster contextualized representations of a word based on (1) the context in which the word appears and (2) the labels of items the word occurs in.
Automated Coding of Under-Studied Medical Concept Domains: Linking Physical Activity Reports to the International Classification of Functioning, Disability, and Health
Both classification and candidate selection approaches present distinct strengths for automated coding in under-studied domains, and we highlight that the combination of (i) a small annotated data set; (ii) expert definitions of codes of interest; and (iii) a representative text corpus is sufficient to produce high-performing automated coding systems.
Handling Class Imbalance in Low-Resource Dialogue Systems by Combining Few-Shot Classification and Interpolation
Utterance classification performance in low-resource dialogue systems is constrained by an inevitably high degree of data imbalance in class labels.
Sequence-to-Set Semantic Tagging for Complex Query Reformulation and Automated Text Categorization in Biomedical IR using Self-Attention
Novel contexts, comprising a set of terms referring to one or more concepts, may often arise in complex querying scenarios such as in evidence-based medicine (EBM) involving biomedical literature.
Towards Real-time Mispronunciation Detection in Kids' Speech
The other loss term uses a uni-directional model as teacher model to align the bi-directional model.
Phonetic Feedback for Speech Enhancement With and Without Parallel Speech Data
While deep learning systems have gained significant ground in speech enhancement research, these systems have yet to make use of the full potential of deep learning systems to provide high-level feedback.
Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction using Neural Attention for Complex Query Reformulation and Automated Text Categorization
Our approach to generate document encodings employing our sequence-to-set models for inference of semantic tags, gives to the best of our knowledge, the state-of-the-art for both, the unsupervised query expansion task for the TREC CDS 2016 challenge dataset when evaluated on an Okapi BM25--based document retrieval system; and also over the MLTM baseline (Soleimani et al, 2016), for both supervised and semi-supervised multi-label prediction tasks on the del. icio. us and Ohsumed datasets.
Learning to Answer Subjective, Specific Product-Related Queries using Customer Reviews by Adversarial Domain Adaptation
Online customer reviews on large-scale e-commerce websites, represent a rich and varied source of opinion data, often providing subjective qualitative assessments of product usage that can help potential customers to discover features that meet their personal needs and preferences.
Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddings
Natural language processing techniques are being applied to increasingly diverse types of electronic health records, and can benefit from in-depth understanding of the distinguishing characteristics of medical document types.
HARE: a Flexible Highlighting Annotator for Ranking and Exploration
Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains.
Characterizing the impact of geometric properties of word embeddings on task performance
Analysis of word embedding properties to inform their use in downstream NLP tasks has largely been studied by assessing nearest neighbors.
An Exploration of Mimic Architectures for Residual Network Based Spectral Mapping
Spectral mapping uses a deep neural network (DNN) to map directly from noisy speech to clean speech.
Sound Audio and Speech Processing
Jointly Embedding Entities and Text with Distant Supervision
Learning representations for knowledge base entities and concepts is becoming increasingly important for NLP applications.
Phrase2VecGLM: Neural generalized language model--based semantic tagging for complex query reformulation in medical IR
In this work, we develop a novel, completely unsupervised, neural language model-based document ranking approach to semantic tagging of documents, using the document to be tagged as a query into the GLM to retrieve candidate phrases from top-ranked related documents, thus associating every document with novel related concepts extracted from the text.
Spectral feature mapping with mimic loss for robust speech recognition
For the task of speech enhancement, local learning objectives are agnostic to phonetic structures helpful for speech recognition.
Cross-Lingual Transfer Learning for POS Tagging without Cross-Lingual Resources
Evaluating on POS datasets from 14 languages in the Universal Dependencies corpus, we show that the proposed transfer learning model improves the POS tagging performance of the target languages without exploiting any linguistic knowledge between the source language and the target language.
Insights into Analogy Completion from the Biomedical Domain
Analogy completion has been a popular task in recent years for evaluating the semantic properties of word embeddings, but the standard methodology makes a number of assumptions about analogies that do not always hold, either in recent benchmark datasets or when expanding into other domains.
Second-Order Word Embeddings from Nearest Neighbor Topological Features
We introduce second-order vector representations of words, induced from nearest neighborhood topological features in pre-trained contextual word embeddings.
How essential are unstructured clinical narratives and information fusion to clinical trial recruitment?
We perform an empirical study to validate the argument and show that structured data alone is insufficient in resolving eligibility criteria for recruiting patients onto clinical trials for chronic lymphocytic leukemia (CLL) and prostate cancer.
Associative and Semantic Features Extracted From Web-Harvested Corpora
We address the problem of automatic classification of associative and semantic relations between words, and particularly those that hold between nouns.
