Search Results for author: Chenguang Zhu

Found 88 papers, 51 papers with code

Modeling Entity Knowledge for Fact Verification

no code implementations EMNLP (FEVER) 2021 Yang Liu, Chenguang Zhu, Michael Zeng

Fact verification is a challenging task of identifying the truthfulness of given claims based on the retrieval of relevant evidence texts.

Descriptive Fact Verification +1

Knowledge-Augmented Methods for Natural Language Processing

no code implementations ACL 2022 Chenguang Zhu, Yichong Xu, Xiang Ren, Bill Lin, Meng Jiang, Wenhao Yu

Knowledge in natural language processing (NLP) has been a rising trend especially after the advent of large scale pre-trained models.

Text Generation

Sparse Modular Activation for Efficient Sequence Modeling

1 code implementation19 Jun 2023 Liliang Ren, Yang Liu, Shuohang Wang, Yichong Xu, Chenguang Zhu, ChengXiang Zhai

Linear State Space Models (SSMs) have demonstrated strong performance in a variety of sequence modeling tasks due to their efficient encoding of the recurrent structure.

Chunking Long-range modeling +1

Fine-Tuning Language Models with Advantage-Induced Policy Alignment

1 code implementation4 Jun 2023 Banghua Zhu, Hiteshi Sharma, Felipe Vieira Frujeri, Shi Dong, Chenguang Zhu, Michael I. Jordan, Jiantao Jiao

Reinforcement learning from human feedback (RLHF) has emerged as a reliable approach to aligning large language models (LLMs) to human preferences.

In-Context Demonstration Selection with Cross Entropy Difference

no code implementations24 May 2023 Dan Iter, Reid Pryzant, Ruochen Xu, Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu

Our method is based on the observation that the effectiveness of in-context demonstrations negatively correlates with the perplexity of the test example by a language model that was finetuned on that demonstration.

Language Modelling Text Generation

PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents

1 code implementation23 May 2023 Simeng Sun, Yang Liu, Shuohang Wang, Chenguang Zhu, Mohit Iyyer

PEARL outperforms zero-shot and chain-of-thought prompting on this dataset, and ablation experiments show that each stage of PEARL is critical to its performance.

i-Code Studio: A Configurable and Composable Framework for Integrative AI

no code implementations23 May 2023 Yuwei Fang, Mahmoud Khademi, Chenguang Zhu, ZiYi Yang, Reid Pryzant, Yichong Xu, Yao Qian, Takuya Yoshioka, Lu Yuan, Michael Zeng, Xuedong Huang

Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities.

Question Answering Retrieval +4

InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT

no code implementations22 May 2023 Yichong Xu, Ruochen Xu, Dan Iter, Yang Liu, Shuohang Wang, Chenguang Zhu, Michael Zeng

While large models such as GPT-3 demonstrate exceptional performance in zeroshot and fewshot summarization tasks, their extensive serving and fine-tuning costs hinder their utilization in various applications.

LMGQS: A Large-scale Dataset for Query-focused Summarization

no code implementations22 May 2023 Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang, Yichong Xu, Dan Iter, Chenguang Zhu, Michael Zeng

We hypothesize that there is a hidden query for each summary sentence in a generic summarization annotation, and we utilize a large-scale pretrained language model to recover it.

Language Modelling

i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data

no code implementations21 May 2023 ZiYi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Mei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang

The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encoder-only models which lack generative abilities.

Any-to-Any Generation via Composable Diffusion

2 code implementations19 May 2023 Zineng Tang, ZiYi Yang, Chenguang Zhu, Michael Zeng, Mohit Bansal

We present Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any combination of input modalities.

Audio Generation

Small Models are Valuable Plug-ins for Large Language Models

1 code implementation15 May 2023 Canwen Xu, Yichong Xu, Shuohang Wang, Yang Liu, Chenguang Zhu, Julian McAuley

Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their weights are often publicly unavailable and their immense sizes make the models difficult to be tuned with common hardware.

Automatic Prompt Optimization with "Gradient Descent" and Beam Search

no code implementations4 May 2023 Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng

Large Language Models (LLMs) have shown impressive performance as general purpose agents, but their abilities remain highly dependent on prompts which are hand written with onerous trial-and-error effort.

G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment

1 code implementation29 Mar 2023 Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, Chenguang Zhu

In this work, we present G-Eval, a framework of using large language models with chain-of-thoughts (CoT) and a form-filling paradigm, to assess the quality of NLG outputs.

Dialogue Generation Text Summarization

How Does In-Context Learning Help Prompt Tuning?

no code implementations22 Feb 2023 Simeng Sun, Yang Liu, Dan Iter, Chenguang Zhu, Mohit Iyyer

This motivates the use of parameter-efficient adaptation methods such as prompt tuning (PT), which adds a small number of tunable embeddings to an otherwise frozen model, and in-context learning (ICL), in which demonstrations of the task are provided to the model in natural language without any additional training.

Text Generation

APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning

no code implementations19 Dec 2022 Soumya Sanyal, Yichong Xu, Shuohang Wang, ZiYi Yang, Reid Pryzant, Wenhao Yu, Chenguang Zhu, Xiang Ren

Logical reasoning of text is an important ability that requires understanding the information present in the text, their interconnections, and then reasoning through them to infer new conclusions.

Data Augmentation Language Modelling +2

Unifying Vision, Text, and Layout for Universal Document Processing

2 code implementations CVPR 2023 Zineng Tang, ZiYi Yang, Guoxin Wang, Yuwei Fang, Yang Liu, Chenguang Zhu, Michael Zeng, Cha Zhang, Mohit Bansal

UDOP leverages the spatial correlation between textual content and document image to model image, text, and layout modalities with one uniform representation.

document understanding Image Reconstruction

Improving Commonsense in Vision-Language Models via Knowledge Graph Riddles

1 code implementation CVPR 2023 Shuquan Ye, Yujia Xie, Dongdong Chen, Yichong Xu, Lu Yuan, Chenguang Zhu, Jing Liao

Through our analysis, we find one important reason is that existing large-scale VL datasets do not contain much commonsense knowledge, which motivates us to improve the commonsense of VL-models from the data perspective.

Data Augmentation Retrieval

UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization

1 code implementation17 Nov 2022 Yulong Chen, Yang Liu, Ruochen Xu, ZiYi Yang, Chenguang Zhu, Michael Zeng, Yue Zhang

The high annotation costs and diverse demands of various summarization tasks motivate the development of few-shot summarization.

MACSum: Controllable Summarization with Mixed Attributes

1 code implementation9 Nov 2022 Yusen Zhang, Yang Liu, ZiYi Yang, Yuwei Fang, Yulong Chen, Dragomir Radev, Chenguang Zhu, Michael Zeng, Rui Zhang

We propose two simple and effective parameter-efficient approaches for the new task of mixed controllable summarization based on hard prompt tuning and soft prefix tuning.


Retrieval Augmentation for Commonsense Reasoning: A Unified Approach

1 code implementation23 Oct 2022 Wenhao Yu, Chenguang Zhu, Zhihan Zhang, Shuohang Wang, Zhuosheng Zhang, Yuwei Fang, Meng Jiang

However, applying such methods to commonsense reasoning tasks faces two unique challenges, i. e., the lack of a general large-scale corpus for retrieval and a corresponding effective commonsense retriever.


Towards a Unified Multi-Dimensional Evaluator for Text Generation

1 code implementation13 Oct 2022 Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, PengFei Liu, Chenguang Zhu, Heng Ji, Jiawei Han

We re-frame NLG evaluation as a Boolean Question Answering (QA) task, and by guiding the model with different questions, we can use one evaluator to evaluate from multiple dimensions.

Question Answering Response Generation +3

Task Compass: Scaling Multi-task Pre-training with Task Prefix

1 code implementation12 Oct 2022 Zhuosheng Zhang, Shuohang Wang, Yichong Xu, Yuwei Fang, Wenhao Yu, Yang Liu, Hai Zhao, Chenguang Zhu, Michael Zeng

Leveraging task-aware annotated data as supervised signals to assist with self-supervised learning on large-scale unlabeled data has become a new trend in pre-training language models.

Data Augmentation Multi-Task Learning +1

A Unified Encoder-Decoder Framework with Entity Memory

1 code implementation7 Oct 2022 Zhihan Zhang, Wenhao Yu, Chenguang Zhu, Meng Jiang

The entity knowledge is stored in the memory as latent representations, and the memory is pre-trained on Wikipedia along with encoder-decoder parameters.

Question Answering Text Generation

Generate rather than Retrieve: Large Language Models are Strong Context Generators

1 code implementation21 Sep 2022 Wenhao Yu, Dan Iter, Shuohang Wang, Yichong Xu, Mingxuan Ju, Soumya Sanyal, Chenguang Zhu, Michael Zeng, Meng Jiang

We call our method generate-then-read (GenRead), which first prompts a large language model to generate contextutal documents based on a given question, and then reads the generated documents to produce the final answer.

Language Modelling Large Language Model +1

REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering

1 code implementation2 Jun 2022 Yuanze Lin, Yujia Xie, Dongdong Chen, Yichong Xu, Chenguang Zhu, Lu Yuan

Specifically, we observe that in most state-of-the-art knowledge-based VQA methods: 1) visual features are extracted either from the whole image or in a sliding window manner for retrieving knowledge, and the important relationship within/among object regions is neglected; 2) visual features are not well utilized in the final answering model, which is counter-intuitive to some extent.

Question Answering Retrieval +1

Leveraging Locality in Abstractive Text Summarization

1 code implementation25 May 2022 Yixin Liu, Ansong Ni, Linyong Nan, Budhaditya Deb, Chenguang Zhu, Ahmed H. Awadallah, Dragomir Radev

Our experimental results show that our model has a better performance compared with strong baselines with efficient attention modules, and our analysis provides further insights into our locality-aware modeling strategy.

Abstractive Text Summarization Text Generation

Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners

1 code implementation22 May 2022 Zhenhailong Wang, Manling Li, Ruochen Xu, Luowei Zhou, Jie Lei, Xudong Lin, Shuohang Wang, ZiYi Yang, Chenguang Zhu, Derek Hoiem, Shih-Fu Chang, Mohit Bansal, Heng Ji

The goal of this work is to build flexible video-language models that can generalize to various video-to-text tasks from few examples, such as domain-specific captioning, question answering, and future event prediction.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +5

Automatic Rule Induction for Interpretable Semi-Supervised Learning

1 code implementation18 May 2022 Reid Pryzant, ZiYi Yang, Yichong Xu, Chenguang Zhu, Michael Zeng

Semi-supervised learning has shown promise in allowing NLP models to generalize from small amounts of labeled data.

Relation Extraction

Impossible Triangle: What's Next for Pre-trained Language Models?

no code implementations13 Apr 2022 Chenguang Zhu, Michael Zeng

Recent development of large-scale pre-trained language models (PLM) have significantly improved the capability of models in various NLP tasks, in terms of performance after task-specific fine-tuning and zero-shot / few-shot learning.

Data Augmentation Few-Shot Learning +2

Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer

no code implementations ACL 2022 Woojeong Jin, Dong-Ho Lee, Chenguang Zhu, Jay Pujara, Xiang Ren

Pre-trained language models are still far from human performance in tasks that need understanding of properties (e. g. appearance, measurable quantity) and affordances of everyday objects in the real world since the text lacks such information due to reporting bias.

Image Captioning Language Modelling +1

SapientML: Synthesizing Machine Learning Pipelines by Learning from Human-Written Solutions

no code implementations18 Feb 2022 Ripon K. Saha, Akira Ura, Sonal Mahajan, Chenguang Zhu, Linyi Li, Yang Hu, Hiroaki Yoshida, Sarfraz Khurshid, Mukul R. Prasad

In this work we propose an AutoML technique SapientML, that can learn from a corpus of existing datasets and their human-written pipelines, and efficiently generate a high-quality pipeline for a predictive task on a new dataset.

AutoML BIG-bench Machine Learning +1

CLIP-Event: Connecting Text and Images with Event Structures

1 code implementation CVPR 2022 Manling Li, Ruochen Xu, Shuohang Wang, Luowei Zhou, Xudong Lin, Chenguang Zhu, Michael Zeng, Heng Ji, Shih-Fu Chang

Vision-language (V+L) pretraining models have achieved great success in supporting multimedia applications by understanding the alignments between images and text.

Contrastive Learning Event Extraction +2

MLP Architectures for Vision-and-Language Modeling: An Empirical Study

1 code implementation8 Dec 2021 Yixin Nie, Linjie Li, Zhe Gan, Shuohang Wang, Chenguang Zhu, Michael Zeng, Zicheng Liu, Mohit Bansal, Lijuan Wang

Based on this, we ask an even bolder question: can we have an all-MLP architecture for VL modeling, where both VL fusion and the vision encoder are replaced with MLPs?

Language Modelling Visual Question Answering (VQA)

Human Parity on CommonsenseQA: Augmenting Self-Attention with External Attention

2 code implementations6 Dec 2021 Yichong Xu, Chenguang Zhu, Shuohang Wang, Siqi Sun, Hao Cheng, Xiaodong Liu, Jianfeng Gao, Pengcheng He, Michael Zeng, Xuedong Huang

In particular, we focus on the task of Commonsense Reasoning, demonstrating that the proposed external attention mechanism can augment existing transformer models and significantly improve the model's reasoning capabilities.

 Ranked #1 on Common Sense Reasoning on CommonsenseQA (using extra training data)

Common Sense Reasoning

SYNERGY: Building Task Bots at Scale Using Symbolic Knowledge and Machine Teaching

no code implementations21 Oct 2021 Baolin Peng, Chunyuan Li, Zhu Zhang, Jinchao Li, Chenguang Zhu, Jianfeng Gao

We propose SYNERGY, a hybrid learning framework where a task bot is developed in two steps: (i) Symbolic knowledge to neural networks: Large amounts of simulated dialog sessions are generated based on task-specific symbolic knowledge which is represented as a task schema consisting of dialog flows and task-oriented databases.

End-to-End Segmentation-based News Summarization

no code implementations Findings (ACL) 2022 Yang Liu, Chenguang Zhu, Michael Zeng

In this paper, we bring a new way of digesting news content by introducing the task of segmenting a news article into multiple sections and generating the corresponding summary to each section.

News Summarization Text Generation

Dict-BERT: Enhancing Language Model Pre-training with Dictionary

1 code implementation Findings (ACL) 2022 Wenhao Yu, Chenguang Zhu, Yuwei Fang, Donghan Yu, Shuohang Wang, Yichong Xu, Michael Zeng, Meng Jiang

In addition to training with the masked language modeling objective, we propose two novel self-supervised pre-training tasks on word and sentence-level alignment between input text sequence and rare word definitions to enhance language modeling representation with dictionary.

Language Modelling Masked Language Modeling

KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering

no code implementations ACL 2022 Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng

The recent proposed Fusion-in-Decoder (FiD), which is built on top of the pretrained generative model T5, achieves the state-of-the-art performance in the reading module.

Answer Generation Open-Domain Question Answering +3

An Exploratory Study on Long Dialogue Summarization: What Works and What's Next

1 code implementation10 Sep 2021 Yusen Zhang, Ansong Ni, Tao Yu, Rui Zhang, Chenguang Zhu, Budhaditya Deb, Asli Celikyilmaz, Ahmed Hassan Awadallah, Dragomir Radev

Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series.


DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization

1 code implementation6 Sep 2021 Ming Zhong, Yang Liu, Yichong Xu, Chenguang Zhu, Michael Zeng

For a dialogue, it corrupts a window of text with dialogue-inspired noise, and guides the model to reconstruct this window based on the content of the remaining conversation.

abstractive question answering Denoising +2

Does Knowledge Help General NLU? An Empirical Study

no code implementations1 Sep 2021 Ruochen Xu, Yuwei Fang, Chenguang Zhu, Michael Zeng

It is often observed in knowledge-centric tasks (e. g., common sense question and answering, relation classification) that the integration of external knowledge such as entity representation into language models can help provide useful information to boost the performance.

Common Sense Reasoning Language Modelling +2

Retrieval Enhanced Model for Commonsense Generation

1 code implementation Findings (ACL) 2021 Han Wang, Yang Liu, Chenguang Zhu, Linjun Shou, Ming Gong, Yichong Xu, Michael Zeng

Commonsense generation is a challenging task of generating a plausible sentence describing an everyday scenario using provided concepts.

Retrieval Text Generation

Sentence-Permuted Paragraph Generation

1 code implementation EMNLP 2021 Wenhao Yu, Chenguang Zhu, Tong Zhao, Zhichun Guo, Meng Jiang

Generating paragraphs of diverse contents is important in many applications.

MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization

1 code implementation NAACL 2021 Chenguang Zhu, Yang Liu, Jie Mei, Michael Zeng

MediaSum, a large-scale media interview dataset consisting of 463. 6K transcripts with abstractive summaries.

Transfer Learning

RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems

no code implementations ACL 2021 Baolin Peng, Chunyuan Li, Zhu Zhang, Chenguang Zhu, Jinchao Li, Jianfeng Gao

For task-oriented dialog systems to be maximally useful, it must be able to process conversations in a way that is (1) generalizable with a small number of training examples for new task domains, and (2) robust to user input in various styles, modalities or domains.

Fusing Context Into Knowledge Graph for Commonsense Question Answering

2 code implementations Findings (ACL) 2021 Yichong Xu, Chenguang Zhu, Ruochen Xu, Yang Liu, Michael Zeng, Xuedong Huang

However, although a KG contains rich structural information, it lacks the context to provide a more precise understanding of the concepts.

Ranked #4 on Common Sense Reasoning on CommonsenseQA (using extra training data)

Common Sense Reasoning Knowledge Graphs +3

A Survey of Knowledge-Enhanced Text Generation

3 code implementations9 Oct 2020 Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, Meng Jiang

To address this issue, researchers have considered incorporating various forms of knowledge beyond the input text into the generation models.

Text Generation

SPLAT: Speech-Language Joint Pre-Training for Spoken Language Understanding

1 code implementation NAACL 2021 Yu-An Chung, Chenguang Zhu, Michael Zeng

Besides conducting a self-supervised masked language modeling task on the two individual modules using unpaired speech and text, SPLAT aligns representations from the two modules in a shared latent space using a small amount of paired speech and text.

Language Modelling Masked Language Modeling +1

Accelerating Real-Time Question Answering via Question Generation

no code implementations10 Sep 2020 Yuwei Fang, Shuohang Wang, Zhe Gan, Siqi Sun, Jingjing Liu, Chenguang Zhu

Although deep neural networks have achieved tremendous success for question answering (QA), they are still suffering from heavy computational and energy cost for real product deployment.

Data Augmentation Multi-Task Learning +3

Mind The Facts: Knowledge-Boosted Coherent Abstractive Text Summarization

no code implementations27 Jun 2020 Beliz Gunel, Chenguang Zhu, Michael Zeng, Xuedong Huang

In this work, we propose a novel architecture that extends Transformer encoder-decoder architecture in order to improve on these shortcomings.

Abstractive Text Summarization Language Modelling

Filtered Inner Product Projection for Crosslingual Embedding Alignment

no code implementations ICLR 2021 Vin Sachidananda, ZiYi Yang, Chenguang Zhu

Due to widespread interest in machine translation and transfer learning, there are numerous algorithms for mapping multiple embeddings to a shared representation space.

Machine Translation Transfer Learning +1

Meta Dialogue Policy Learning

no code implementations3 Jun 2020 Yumo Xu, Chenguang Zhu, Baolin Peng, Michael Zeng

Dialog policy determines the next-step actions for agents and hence is central to a dialogue system.

Meta-Learning Transfer Learning

Boosting Naturalness of Language in Task-oriented Dialogues via Adversarial Training

no code implementations SIGDIAL (ACL) 2020 Chenguang Zhu

The natural language generation (NLG) module in a task-oriented dialogue system produces user-facing utterances conveying required information.

Text Generation

Few-shot Natural Language Generation for Task-Oriented Dialog

1 code implementation Findings of the Association for Computational Linguistics 2020 Baolin Peng, Chenguang Zhu, Chunyuan Li, Xiujun Li, Jinchao Li, Michael Zeng, Jianfeng Gao

It is pre-trained on a large set of annotated NLG corpus to acquire the controllable generation ability, and fine-tuned with only a few domain-specific labels to adapt to new domains.

Data-to-Text Generation Few-Shot Learning

Leveraging Lead Bias for Zero-shot Abstractive News Summarization

no code implementations25 Dec 2019 Chenguang Zhu, Ziyi Yang, Robert Gmyr, Michael Zeng, Xuedong Huang

A typical journalistic convention in news articles is to deliver the most salient information in the beginning, also known as the lead bias.

Domain Adaptation News Summarization

SIM: A Slot-Independent Neural Model for Dialogue State Tracking

no code implementations WS 2019 Chenguang Zhu, Michael Zeng, Xuedong Huang

In this paper, we put forward a slot-independent neural model (SIM) to track dialogue states while keeping the model complexity invariant to the number of dialogue slots.

Dialogue State Tracking Task-Oriented Dialogue Systems

Make Lead Bias in Your Favor: A Simple and Effective Method for News Summarization

no code implementations25 Sep 2019 Chenguang Zhu, ZiYi Yang, Robert Gmyr, Michael Zeng, Xuedong Huang

For example, the pretrained model without finetuning outperforms pointer-generator network on CNN/DailyMail dataset.

News Summarization

Embedding Imputation with Grounded Language Information

1 code implementation ACL 2019 Ziyi Yang, Chenguang Zhu, Sachidan, Vin a, Eric Darve

In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph.


Out-of-Vocabulary Embedding Imputation with Grounded Language Information by Graph Convolutional Networks

no code implementations ACL 2019 Ziyi Yang, Chenguang Zhu, Vin Sachidananda, Eric Darve

In this paper, we propose an approach for embedding imputation which uses grounded information in the form of a knowledge graph.


Parameter-free Sentence Embedding via Orthogonal Basis

1 code implementation IJCNLP 2019 Ziyi Yang, Chenguang Zhu, Weizhu Chen

Inspired by the Gram-Schmidt Process in geometric theory, we build an orthogonal basis of the subspace spanned by a word and its surrounding context in a sentence.

Sentence Embedding Sentence-Embedding +1

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