343 research outputs found
Graph Neural Networks for Natural Language Processing: A Survey
Deep learning has become the dominant approach in coping with various tasks
in Natural LanguageProcessing (NLP). Although text inputs are typically
represented as a sequence of tokens, there isa rich variety of NLP problems
that can be best expressed with a graph structure. As a result, thereis a surge
of interests in developing new deep learning techniques on graphs for a large
numberof NLP tasks. In this survey, we present a comprehensive overview onGraph
Neural Networks(GNNs) for Natural Language Processing. We propose a new
taxonomy of GNNs for NLP, whichsystematically organizes existing research of
GNNs for NLP along three axes: graph construction,graph representation
learning, and graph based encoder-decoder models. We further introducea large
number of NLP applications that are exploiting the power of GNNs and summarize
thecorresponding benchmark datasets, evaluation metrics, and open-source codes.
Finally, we discussvarious outstanding challenges for making the full use of
GNNs for NLP as well as future researchdirections. To the best of our
knowledge, this is the first comprehensive overview of Graph NeuralNetworks for
Natural Language Processing.Comment: 127 page
Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications
With the abundant amount of available online and offline text data, there
arises a crucial need to extract the relation between phrases and summarize the
main content of each document in a few words. For this purpose, there have been
many studies recently in Open Information Extraction (OIE). OIE improves upon
relation extraction techniques by analyzing relations across different domains
and avoids requiring hand-labeling pre-specified relations in sentences. This
paper surveys recent approaches of OIE and its applications on Knowledge Graph
(KG), text summarization, and Question Answering (QA). Moreover, the paper
describes OIE basis methods in relation extraction. It briefly discusses the
main approaches and the pros and cons of each method. Finally, it gives an
overview about challenges, open issues, and future work opportunities for OIE,
relation extraction, and OIE applications.Comment: 15 pages, 9 figure
Multilingual Unsupervised Sentence Simplification
Progress in Sentence Simplification has been hindered by the lack of
supervised data, particularly in languages other than English. Previous work
has aligned sentences from original and simplified corpora such as English
Wikipedia and Simple English Wikipedia, but this limits corpus size, domain,
and language. In this work, we propose using unsupervised mining techniques to
automatically create training corpora for simplification in multiple languages
from raw Common Crawl web data. When coupled with a controllable generation
mechanism that can flexibly adjust attributes such as length and lexical
complexity, these mined paraphrase corpora can be used to train simplification
systems in any language. We further incorporate multilingual unsupervised
pretraining methods to create even stronger models and show that by training on
mined data rather than supervised corpora, we outperform the previous best
results. We evaluate our approach on English, French, and Spanish
simplification benchmarks and reach state-of-the-art performance with a totally
unsupervised approach. We will release our models and code to mine the data in
any language included in Common Crawl
深層学習に基づく感情会話分析に関する研究
Owning the capability to express specific emotions by a chatbot during a conversation is one of the key parts of artificial intelligence, which has an intuitive and quantifiable impact on the improvement of chatbot’s usability and user satisfaction. Enabling machines to emotion recognition in conversation is challenging, mainly because the information in human dialogue innately conveys emotions by long-term experience, abundant knowledge, context, and the intricate patterns between the affective states. Recently, many studies on neural emotional conversational models have been conducted. However, enabling the chatbot to control what kind of emotion to respond to upon its own characters in conversation is still underexplored. At this stage, people are no longer satisfied with using a dialogue system to solve specific tasks, and are more eager to achieve spiritual communication. In the chat process, if the robot can perceive the user's emotions and can accurately process them, it can greatly enrich the content of the dialogue and make the user empathize.
In the process of emotional dialogue, our ultimate goal is to make the machine understand human emotions and give matching responses. Based on these two points, this thesis explores and in-depth emotion recognition in conversation task and emotional dialogue generation task. In the past few years, although considerable progress has been made in emotional research in dialogue, there are still some difficulties and challenges due to the complex nature of human emotions. The key contributions in this thesis are summarized as below:
(1) Researchers have paid more attention to enhancing natural language models with knowledge graphs these days, since knowledge graph has gained a lot of systematic knowledge. A large number of studies had shown that the introduction of external commonsense knowledge is very helpful to improve the characteristic information. We address the task of emotion recognition in conversations using external knowledge to enhance semantics. In this work, we employ an external knowledge graph ATOMIC to extract the knowledge sources. We proposed KES model, a new framework that incorporates different elements of external knowledge and conversational semantic role labeling, where build upon them to learn interactions between interlocutors participating in a conversation. The conversation is a sequence of coherent and orderly discourses. For neural networks, the capture of long-range context information is a weakness. We adopt Transformer a structure composed of self-attention and feed forward neural network, instead of the traditional RNN model, aiming at capturing remote context information. We design a self-attention layer specialized for enhanced semantic text features with external commonsense knowledge. Then, two different networks composed of LSTM are responsible for tracking individual internal state and context external state. In addition, the proposed model has experimented on three datasets in emotion detection in conversation. The experimental results show that our model outperforms the state-of-the-art approaches on most of the tested datasets.
(2) We proposed an emotional dialogue model based on Seq2Seq, which is improved from three aspects: model input, encoder structure, and decoder structure, so that the model can generate responses with rich emotions, diversity, and context. In terms of model input, emotional information and location information are added based on word vectors. In terms of the encoder, the proposed model first encodes the current input and sentence sentiment to generate a semantic vector, and additionally encodes the context and sentence sentiment to generate a context vector, adding contextual information while ensuring the independence of the current input. On the decoder side, attention is used to calculate the weights of the two semantic vectors separately and then decode, to fully integrate the local emotional semantic information and the global emotional semantic information. We used seven objective evaluation indicators to evaluate the model's generation results, context similarity, response diversity, and emotional response. Experimental results show that the model can generate diverse responses with rich sentiment, contextual associations
Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence Models
Keyphrase Generation (KPG) is a longstanding task in NLP with widespread
applications. The advent of sequence-to-sequence (seq2seq) pre-trained language
models (PLMs) has ushered in a transformative era for KPG, yielding promising
performance improvements. However, many design decisions remain unexplored and
are often made arbitrarily. This paper undertakes a systematic analysis of the
influence of model selection and decoding strategies on PLM-based KPG. We begin
by elucidating why seq2seq PLMs are apt for KPG, anchored by an
attention-driven hypothesis. We then establish that conventional wisdom for
selecting seq2seq PLMs lacks depth: (1) merely increasing model size or
performing task-specific adaptation is not parameter-efficient; (2) although
combining in-domain pre-training with task adaptation benefits KPG, it does
partially hinder generalization. Regarding decoding, we demonstrate that while
greedy search achieves strong F1 scores, it lags in recall compared with
sampling-based methods. Based on these insights, we propose DeSel, a
likelihood-based decode-select algorithm for seq2seq PLMs. DeSel improves
greedy search by an average of 4.7% semantic F1 across five datasets. Our
collective findings pave the way for deeper future investigations into
PLM-based KPG.Comment: EMNLP 2023 camera read
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