pwc-archive/papers-with-abstracts huggingface dataset:
Block the Label and Noise: An N-Gram Masked Speller for Chinese Spell Checking
Authors: Haiyun Yang
Date: 2023-05-05 00:00:00
Tasks: Chinese Spell Checking, Sentence

Recently, Chinese Spell Checking(CSC), a task to detect erroneous characters in a sentence and correct them, has attracted extensive interest because of its wide applications in various NLP tasks. Most of the existing methods have utilized BERT to extract semantic information for CSC task. However, these methods directly take sentences with only a few errors as inputs, where the correct characters may leak answers to the model and dampen its ability to capture distant context; while the erroneous characters may disturb the semantic encoding process and result in poor representations. Based on such observations, this paper proposes an n-gram masking layer that masks current and/or surrounding tokens to avoid label leakage and error disturbance. Moreover, considering that the mask strategy may ignore multi-modal information indicated by errors, a novel dot-product gating mechanism is proposed to integrate the phonological and morphological information with semantic representation. Extensive experiments on SIGHAN datasets have demonstrated that the pluggable n-gram masking mechanism can improve the performance of prevalent CSC models and the proposed methods in this paper outperform multiple powerful state-of-the-art models.
LLM-RM at SemEval-2023 Task 2: Multilingual Complex NER using XLM-RoBERTa
Authors: Rahul Mehta, Vasudeva Varma
Date: 2023-05-05 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence, Task 2

Named Entity Recognition(NER) is a task of recognizing entities at a token level in a sentence. This paper focuses on solving NER tasks in a multilingual setting for complex named entities. Our team, LLM-RM participated in the recently organized SemEval 2023 task, Task 2: MultiCoNER II,Multilingual Complex Named Entity Recognition. We approach the problem by leveraging cross-lingual representation provided by fine-tuning XLM-Roberta base model on datasets of all of the 12 languages provided -- Bangla, Chinese, English, Farsi, French, German, Hindi, Italian, Portuguese, Spanish, Swedish and Ukrainian
TransESC: Smoothing Emotional Support Conversation via Turn-Level State Transition
Authors: Weixiang Zhao, Yanyan Zhao, Shilong Wang, Bing Qin
Date: 2023-05-05 00:00:00
Tasks: Decoder

Emotion Support Conversation (ESC) is an emerging and challenging task with the goal of reducing the emotional distress of people. Previous attempts fail to maintain smooth transitions between utterances in ESC because they ignore to grasp the fine-grained transition information at each dialogue turn. To solve this problem, we propose to take into account turn-level state \textbf{Trans}itions of \textbf{ESC} (\textbf{TransESC}) from three perspectives, including semantics transition, strategy transition and emotion transition, to drive the conversation in a smooth and natural way. Specifically, we construct the state transition graph with a two-step way, named transit-then-interact, to grasp such three types of turn-level transition information. Finally, they are injected into the transition-aware decoder to generate more engaging responses. Both automatic and human evaluations on the benchmark dataset demonstrate the superiority of TransESC to generate more smooth and effective supportive responses. Our source code is available at \url{https://github.com/circle-hit/TransESC}.
Expository Text Generation: Imitate, Retrieve, Paraphrase
Authors: Nishant Balepur, Jie Huang, Kevin Chen-Chuan Chang
Date: 2023-05-05 00:00:00
Tasks: Fact Selection, Retrieval, Text Generation

Expository documents are vital resources for conveying complex information to readers. Despite their usefulness, writing expository text by hand is a challenging process that requires careful content planning, obtaining facts from multiple sources, and the ability to clearly synthesize these facts. To ease these burdens, we propose the task of expository text generation, which seeks to automatically generate an accurate and stylistically consistent expository text for a topic by intelligently searching a knowledge source. We solve our task by developing IRP, a framework that overcomes the limitations of retrieval-augmented models and iteratively performs content planning, fact retrieval, and rephrasing. Through experiments on three diverse, newly-collected datasets, we show that IRP produces factual and organized expository texts that accurately inform readers.
Verify-and-Edit: A Knowledge-Enhanced Chain-of-Thought Framework
Authors: Ruochen Zhao, Xingxuan Li, Shafiq Joty, Chengwei Qin, Lidong Bing
Date: 2023-05-05 00:00:00
Tasks: Open-Domain Question Answering, Question Answering

As large language models (LLMs) have become the norm in NLP, demonstrating good performance in generation and reasoning tasks, one of its most fatal disadvantages is the lack of factual correctness. Generating unfactual texts not only leads to lower performances but also degrades the trust and validity of their applications. Chain-of-Thought (CoT) prompting improves trust and model performance on complex reasoning tasks by generating interpretable reasoning chains, but still suffers from factuality concerns in knowledge-intensive tasks. In this paper, we propose the Verify-and-Edit framework for CoT prompting, which seeks to increase prediction factuality by post-editing reasoning chains according to external knowledge. Building on top of GPT-3, our framework lead to accuracy improvements in multiple open-domain question-answering tasks.
Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization
Authors: Yangyang Zhao, Zhenyu Wang, Mehdi Dastani, Shihan Wang
Date: 2023-05-05 00:00:00
Tasks: Data Augmentation, Deep Reinforcement Learning, Efficient Exploration

Training a dialogue policy using deep reinforcement learning requires a lot of exploration of the environment. The amount of wasted invalid exploration makes their learning inefficient. In this paper, we find and define an important reason for the invalid exploration: dead-ends. When a conversation enters a dead-end state, regardless of the actions taken afterward, it will continue in a dead-end trajectory until the agent reaches a termination state or maximum turn. We propose a dead-end resurrection (DDR) algorithm that detects the initial dead-end state in a timely and efficient manner and provides a rescue action to guide and correct the exploration direction. To prevent dialogue policies from repeatedly making the same mistake, DDR also performs dialogue data augmentation by adding relevant experiences containing dead-end states. We first validate the dead-end detection reliability and then demonstrate the effectiveness and generality of the method by reporting experimental results on several dialogue datasets from different domains.
Stylized Data-to-Text Generation: A Case Study in the E-Commerce Domain
Authors: Liqiang Jing, Xuemeng Song, Xuming Lin, Zhongzhou Zhao, Wei Zhou, Liqiang Nie
Date: 2023-05-05 00:00:00
Tasks: Attribute, Data-to-Text Generation, Text Generation, Triplet

Existing data-to-text generation efforts mainly focus on generating a coherent text from non-linguistic input data, such as tables and attribute-value pairs, but overlook that different application scenarios may require texts of different styles. Inspired by this, we define a new task, namely stylized data-to-text generation, whose aim is to generate coherent text for the given non-linguistic data according to a specific style. This task is non-trivial, due to three challenges: the logic of the generated text, unstructured style reference, and biased training samples. To address these challenges, we propose a novel stylized data-to-text generation model, named StyleD2T, comprising three components: logic planning-enhanced data embedding, mask-based style embedding, and unbiased stylized text generation. In the first component, we introduce a graph-guided logic planner for attribute organization to ensure the logic of generated text. In the second component, we devise feature-level mask-based style embedding to extract the essential style signal from the given unstructured style reference. In the last one, pseudo triplet augmentation is utilized to achieve unbiased text generation, and a multi-condition based confidence assignment function is designed to ensure the quality of pseudo samples. Extensive experiments on a newly collected dataset from Taobao have been conducted, and the results show the superiority of our model over existing methods.
VicunaNER: Zero/Few-shot Named Entity Recognition using Vicuna
Authors: Bin Ji
Date: 2023-05-05 00:00:00
Tasks: few-shot-ner, Few-shot NER, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Large Language Models (LLMs, e.g., ChatGPT) have shown impressive zero- and few-shot capabilities in Named Entity Recognition (NER). However, these models can only be accessed via online APIs, which may cause data leak and non-reproducible problems. In this paper, we propose VicunaNER, a zero/few-shot NER framework based on the newly released open-source LLM -- Vicuna. VicunaNER is a two-phase framework, where each phase leverages multi-turn dialogues with Vicuna to recognize entities from texts. We name the second phase as Re-Recognition, which recognizes those entities not recognized in the first phase (a.k.a. Recognition). Moreover, we set entity correctness check dialogues in each phase to filter out wrong entities. We evaluate VicunaNER's zero-shot capacity on 10 datasets crossing 5 domains and few-shot capacity on Few-NERD. Experimental results demonstrate that VicunaNER achieves superior performance in both shot settings. Additionally, we conduct comprehensive investigations on Vicuna from multiple perspectives.
Neuromodulation Gated Transformer
Authors: Kobe Knowles, Joshua Bensemann, Diana Benavides-Prado, Vithya Yogarajan, Michael Witbrock, Gillian Dobbie, Yang Chen
Date: 2023-05-05 00:00:00

We introduce a novel architecture, the Neuromodulation Gated Transformer (NGT), which is a simple implementation of neuromodulation in transformers via a multiplicative effect. We compare it to baselines and show that it results in the best average performance on the SuperGLUE benchmark validation sets.
Employing Hybrid Deep Neural Networks on Dari Speech
Authors: Jawid Ahmad Baktash, Mursal Dawodi
Date: 2023-05-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper is an extension of our previous conference paper. In recent years, there has been a growing interest among researchers in developing and improving speech recognition systems to facilitate and enhance human-computer interaction. Today, Automatic Speech Recognition (ASR) systems have become ubiquitous, used in everything from games to translation systems, robots, and more. However, much research is still needed on speech recognition systems for low-resource languages. This article focuses on the recognition of individual words in the Dari language using the Mel-frequency cepstral coefficients (MFCCs) feature extraction method and three different deep neural network models: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Multilayer Perceptron (MLP), as well as two hybrid models combining CNN and RNN. We evaluate these models using an isolated Dari word corpus that we have created, consisting of 1000 utterances for 20 short Dari terms. Our study achieved an impressive average accuracy of 98.365%.
The Role of Global and Local Context in Named Entity Recognition
Authors: Arthur Amalvy, Vincent Labatut, Richard Dufour
Date: 2023-05-04 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Pre-trained transformer-based models have recently shown great performance when applied to Named Entity Recognition (NER). As the complexity of their self-attention mechanism prevents them from processing long documents at once, these models are usually applied in a sequential fashion. Such an approach unfortunately only incorporates local context and prevents leveraging global document context in long documents such as novels, which might hinder performance. In this article, we explore the impact of global document context, and its relationships with local context. We find that correctly retrieving global document context has a greater impact on performance than only leveraging local context, prompting for further research on how to better retrieve that context.
Chain-of-Skills: A Configurable Model for Open-domain Question Answering
Authors: Kaixin Ma, Hao Cheng, Yu Zhang, Xiaodong Liu, Eric Nyberg, Jianfeng Gao
Date: 2023-05-04 00:00:00
Tasks: Open-Domain Question Answering, Question Answering, Retrieval, World Knowledge

The retrieval model is an indispensable component for real-world knowledge-intensive tasks, e.g., open-domain question answering (ODQA). As separate retrieval skills are annotated for different datasets, recent work focuses on customized methods, limiting the model transferability and scalability. In this work, we propose a modular retriever where individual modules correspond to key skills that can be reused across datasets. Our approach supports flexible skill configurations based on the target domain to boost performance. To mitigate task interference, we design a novel modularization parameterization inspired by sparse Transformer. We demonstrate that our model can benefit from self-supervised pretraining on Wikipedia and fine-tuning using multiple ODQA datasets, both in a multi-task fashion. Our approach outperforms recent self-supervised retrievers in zero-shot evaluations and achieves state-of-the-art fine-tuned retrieval performance on NQ, HotpotQA and OTT-QA.
Are Human Explanations Always Helpful? Towards Objective Evaluation of Human Natural Language Explanations
Authors: Bingsheng Yao, Prithviraj Sen, Lucian Popa, James Hendler, Dakuo Wang
Date: 2023-05-04 00:00:00

Human-annotated labels and explanations are critical for training explainable NLP models. However, unlike human-annotated labels whose quality is easier to calibrate (e.g., with a majority vote), human-crafted free-form explanations can be quite subjective. Before blindly using them as ground truth to train ML models, a vital question needs to be asked: How do we evaluate a human-annotated explanation's quality? In this paper, we build on the view that the quality of a human-annotated explanation can be measured based on its helpfulness (or impairment) to the ML models' performance for the desired NLP tasks for which the annotations were collected. In comparison to the commonly used Simulatability score, we define a new metric that can take into consideration the helpfulness of an explanation for model performance at both fine-tuning and inference. With the help of a unified dataset format, we evaluated the proposed metric on five datasets (e.g., e-SNLI) against two model architectures (T5 and BART), and the results show that our proposed metric can objectively evaluate the quality of human-annotated explanations, while Simulatability falls short.
Hybrid Transducer and Attention based Encoder-Decoder Modeling for Speech-to-Text Tasks
Authors: Yun Tang, Anna Y. Sun, Hirofumi Inaguma, Xinyue Chen, Ning Dong, Xutai Ma, Paden D. Tomasello, Juan Pino
Date: 2023-05-04 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modelling, speech-recognition, Speech Recognition, Speech-to-Text, Speech-to-Text Translation, Translation

Transducer and Attention based Encoder-Decoder (AED) are two widely used frameworks for speech-to-text tasks. They are designed for different purposes and each has its own benefits and drawbacks for speech-to-text tasks. In order to leverage strengths of both modeling methods, we propose a solution by combining Transducer and Attention based Encoder-Decoder (TAED) for speech-to-text tasks. The new method leverages AED's strength in non-monotonic sequence to sequence learning while retaining Transducer's streaming property. In the proposed framework, Transducer and AED share the same speech encoder. The predictor in Transducer is replaced by the decoder in the AED model, and the outputs of the decoder are conditioned on the speech inputs instead of outputs from an unconditioned language model. The proposed solution ensures that the model is optimized by covering all possible read/write scenarios and creates a matched environment for streaming applications. We evaluate the proposed approach on the \textsc{MuST-C} dataset and the findings demonstrate that TAED performs significantly better than Transducer for offline automatic speech recognition (ASR) and speech-to-text translation (ST) tasks. In the streaming case, TAED outperforms Transducer in the ASR task and one ST direction while comparable results are achieved in another translation direction.
Curating corpora with classifiers: A case study of clean energy sentiment online
Authors: Michael V. Arnold, Peter Sheridan Dodds, Christopher M. Danforth
Date: 2023-05-04 00:00:00
Tasks: Binary Classification

Well curated, large-scale corpora of social media posts containing broad public opinion offer an alternative data source to complement traditional surveys. While surveys are effective at collecting representative samples and are capable of achieving high accuracy, they can be both expensive to run and lag public opinion by days or weeks. Both of these drawbacks could be overcome with a real-time, high volume data stream and fast analysis pipeline. A central challenge in orchestrating such a data pipeline is devising an effective method for rapidly selecting the best corpus of relevant documents for analysis. Querying with keywords alone often includes irrelevant documents that are not easily disambiguated with bag-of-words natural language processing methods. Here, we explore methods of corpus curation to filter irrelevant tweets using pre-trained transformer-based models, fine-tuned for our binary classification task on hand-labeled tweets. We are able to achieve F1 scores of up to 0.95. The low cost and high performance of fine-tuning such a model suggests that our approach could be of broad benefit as a pre-processing step for social media datasets with uncertain corpus boundaries.
Leveraging BERT Language Model for Arabic Long Document Classification
Authors: Muhammad Al-Qurishi
Date: 2023-05-04 00:00:00
Tasks: Classification, Document Classification, Language Modeling, Language Modelling

Given the number of Arabic speakers worldwide and the notably large amount of content in the web today in some fields such as law, medicine, or even news, documents of considerable length are produced regularly. Classifying those documents using traditional learning models is often impractical since extended length of the documents increases computational requirements to an unsustainable level. Thus, it is necessary to customize these models specifically for long textual documents. In this paper we propose two simple but effective models to classify long length Arabic documents. We also fine-tune two different models-namely, Longformer and RoBERT, for the same task and compare their results to our models. Both of our models outperform the Longformer and RoBERT in this task over two different datasets.
Explore the difficulty of words and its influential attributes based on the Wordle game
Authors: Beibei Liu, Yuanfang Zhang, Shiyu Zhang
Date: 2023-05-03 00:00:00
Tasks: regression

We adopt the distribution and expectation of guessing times in game Wordle as metrics to predict the difficulty of words and explore their influence factors. In order to predictthe difficulty distribution, we use Monte Carlo to simulate the guessing process of players and then narrow the gap between raw and actual distribution of guessing times for each word with Markov which generates the associativity of words. Afterwards, we take advantage of lasso regression to predict the deviation of guessing times expectation and quadratic programming to obtain the correction of the original distribution.To predict the difficulty levels, we first use hierarchical clustering to classify the difficulty levels based on the expectation of guessing times. Afterwards we downscale the variables of lexical attributes based on factor analysis. Significant factors include the number of neighboring words, letter similarity, sub-string similarity, and word frequency. Finally, we build the relationship between lexical attributes and difficulty levels through ordered logistic regression.
Improving Cancer Hallmark Classification with BERT-based Deep Learning Approach
Authors: Sultan Zavrak, Seyhmus Yilmaz
Date: 2023-05-02 00:00:00
Tasks: Deep Learning, Transfer Learning

This paper presents a novel approach to accurately classify the hallmarks of cancer, which is a crucial task in cancer research. Our proposed method utilizes the Bidirectional Encoder Representations from Transformers (BERT) architecture, which has shown exceptional performance in various downstream applications. By applying transfer learning, we fine-tuned the pre-trained BERT model on a small corpus of biomedical text documents related to cancer. The outcomes of our experimental investigations demonstrate that our approach attains a noteworthy accuracy of 94.45%, surpassing almost all prior findings with a substantial increase of at least 8.04% as reported in the literature. These findings highlight the effectiveness of our proposed model in accurately classifying and comprehending text documents for cancer research, thus contributing significantly to the field. As cancer remains one of the top ten leading causes of death globally, our approach holds great promise in advancing cancer research and improving patient outcomes.
Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction
Authors: Xuming Hu, Zhaochen Hong, Chenwei Zhang, Irwin King, Philip S. Yu
Date: 2023-05-02 00:00:00
Tasks: counterfactual, Relation, Relation Extraction, Sentence

Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role. Previous works either focus on how to leverage the entity information (e.g., entity types, entity verbalization) to inference relations, but ignore context-focused content, or use counterfactual thinking to remove the model's bias of potential relations in entities, but the relation reasoning process will still be hindered by irrelevant content. Therefore, how to preserve relevant content and remove noisy segments from sentences is a crucial task. In addition, retained content needs to be fluent enough to maintain semantic coherence and interpretability. In this work, we propose a novel rationale extraction framework named RE2, which leverages two continuity and sparsity factors to obtain relevant and coherent rationales from sentences. To solve the problem that the gold rationales are not labeled, RE2 applies an optimizable binary mask to each token in the sentence, and adjust the rationales that need to be selected according to the relation label. Experiments on four datasets show that RE2 surpasses baselines.