Search Results for author:
Found 53 papers, 20 papers with code
Text2Mol: Cross-Modal Molecule Retrieval with Natural Language Queries
Moreover, this can be viewed as an especially challenging cross-lingual retrieval problem by considering the molecules as a language with a very unique grammar.
Ranked #2 on
Cross-Modal Retrieval
on ChEBI-20
Prejudice and Caprice: A Statistical Framework for Measuring Social Discrimination in Large Language Models
The growing integration of large language models (LLMs) into social operations amplifies their impact on decisions in crucial areas such as economics, law, education, and healthcare, raising public concerns about these models' discrimination-related safety and reliability.
Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement
The increasing demand for personalized interactions with large language models (LLMs) calls for the development of methodologies capable of accurately and efficiently identifying user opinions and preferences.
Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains
In this paper, we study the task of seed-guided fine-grained entity typing in science and engineering domains, which takes the name and a few seed entities for each entity type as the only supervision and aims to classify new entity mentions into both seen and unseen types (i. e., those without seed entities).
If LLM Is the Wizard, Then Code Is the Wand: A Survey on How Code Empowers Large Language Models to Serve as Intelligent Agents
The prominent large language models (LLMs) of today differ from past language models not only in size, but also in the fact that they are trained on a combination of natural language and formal language (code).
Social Commonsense-Guided Search Query Generation for Open-Domain Knowledge-Powered Conversations
Open-domain dialog involves generating search queries that help obtain relevant knowledge for holding informative conversations.
Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting
Automatic response forecasting for news media plays a crucial role in enabling content producers to efficiently predict the impact of news releases and prevent unexpected negative outcomes such as social conflict and moral injury.
C-PMI: Conditional Pointwise Mutual Information for Turn-level Dialogue Evaluation
Existing reference-free turn-level evaluation metrics for chatbots inadequately capture the interaction between the user and the system.
Sparse Modular Activation for Efficient Sequence Modeling
To validate the effectiveness of SMA on sequence modeling, we design a novel neural architecture, SeqBoat, which employs SMA to sparsely activate a Gated Attention Unit (GAU) based on the state representations learned from an SSM.
Ranked #2 on
Long-range modeling
on LRA
User Simulation for Evaluating Information Access Systems
Information access systems, such as search engines, recommender systems, and conversational assistants, have become integral to our daily lives as they help us satisfy our information needs.
Measuring the Effect of Influential Messages on Varying Personas
Our analysis shows that the best-performing models are capable of predicting responses that are consistent with the personas, and as a byproduct, the task formulation also enables many interesting applications in the analysis of social network groups and their opinions, such as the discovery of extreme opinion groups.
Noise-Robust Dense Retrieval via Contrastive Alignment Post Training
The success of contextual word representations and advances in neural information retrieval have made dense vector-based retrieval a standard approach for passage and document ranking.
To Asymmetry and Beyond: Structured Pruning of Sequence to Sequence Models for Improved Inference Efficiency
Sequence-to-sequence language models can be used to produce abstractive summaries which are coherent, relevant, and concise.
Quick Dense Retrievers Consume KALE: Post Training Kullback Leibler Alignment of Embeddings for Asymmetrical dual encoders
In this paper, we consider the problem of improving the inference latency of language model-based dense retrieval systems by introducing structural compression and model size asymmetry between the context and query encoders.
Dense Sparse Retrieval: Using Sparse Language Models for Inference Efficient Dense Retrieval
Vector-based retrieval systems have become a common staple for academic and industrial search applications because they provide a simple and scalable way of extending the search to leverage contextual representations for documents and queries.
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes
In this paper, we introduce the range of oBERTa language models, an easy-to-use set of language models which allows Natural Language Processing (NLP) practitioners to obtain between 3. 8 and 24. 3 times faster models without expertise in model compression.
Competence-Based Analysis of Language Models
Our framework, CALM (Competence-based Analysis of Language Models), establishes the first quantitative measure of LLM competence, which we study by damaging models' internal representations of various linguistic properties in the course of performing various tasks using causal probing and evaluating models' alignment under these interventions with a given causal model.
Learning by Applying: A General Framework for Mathematical Reasoning via Enhancing Explicit Knowledge Learning
In LeAp, we perform knowledge learning in a novel problem-knowledge-expression paradigm, with a Knowledge Encoder to acquire knowledge from problem data and a Knowledge Decoder to apply knowledge for expression reasoning.
Entity Set Co-Expansion in StackOverflow
Given a few seed entities of a certain type (e. g., Software or Programming Language), entity set expansion aims to discover an extensive set of entities that share the same type as the seeds.
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications
Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks.
Language Model Pre-Training with Sparse Latent Typing
Modern large-scale Pre-trained Language Models (PLMs) have achieved tremendous success on a wide range of downstream tasks.
Ranked #6 on
Few-shot NER
on Few-NERD (INTRA)
(using extra training data)
Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
We propose a novel application of prompting Pre-trained Language Models (PLMs) to generate analogies and study how to design effective prompts for two task settings: generating a source concept analogous to a given target concept (aka Analogous Concept Generation or ACG), and generating an explanation of the similarity between a given pair of target concept and source concept (aka Analogous Explanation Generation or AEG).
CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval
Given the absence of cross-lingual information retrieval datasets with claim-like queries, we train the retriever with our proposed Cross-lingual Inverse Cloze Task (X-ICT), a self-supervised algorithm that creates training instances by translating the title of a passage.
Ranked #1 on
Zero-shot Cross-lingual Fact-checking
on X-Fact
Cross-lingual Fact-checking
Cross-Lingual Information Retrieval
+4
Incorporating Task-specific Concept Knowledge into Script Learning
In this paper, we present Tetris, a new task of Goal-Oriented Script Completion.
Sparse*BERT: Sparse Models Generalize To New tasks and Domains
Our experimentation shows that models that are pruned during pretraining using general domain masked language models can transfer to novel domains and tasks without extensive hyperparameter exploration or specialized approaches.
Domain Representative Keywords Selection: A Probabilistic Approach
We propose a probabilistic approach to select a subset of a \textit{target domain representative keywords} from a candidate set, contrasting with a context domain.
Improving Candidate Retrieval with Entity Profile Generation for Wikidata Entity Linking
We use the profile to query the indexed search engine to retrieve candidate entities.
BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks
Biomedical entity linking is the task of linking entity mentions in a biomedical document to referent entities in a knowledge base.
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation
Automated knowledge discovery from trending chemical literature is essential for more efficient biomedical research.
Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference
It then uses an entity linker to form a knowledge graph containing relevant background knowledge for the the entity mentions in the text.
DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization
The recent growth of web video sharing platforms has increased the demand for systems that can efficiently browse, retrieve and summarize video content.
No Feature Is An Island: Adaptive Collaborations Between Features Improve Adversarial Robustness
To classify images, neural networks extract features from raw inputs and then sum them up with fixed weights via the fully connected layer.
Towards Dark Jargon Interpretation in Underground Forums
Dark jargons are benign-looking words that have hidden, sinister meanings and are used by participants of underground forums for illicit behavior.
AutoML to Date and Beyond: Challenges and Opportunities
AutoML tools aim to make machine learning accessible for non-machine learning experts (domain experts), to improve the efficiency of machine learning, and to accelerate machine learning research.
Drink Bleach or Do What Now? Covid-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformation
Several works study health misinformation detection, yet little attention has been given to the perceived severity of misinformation posts.
Multi-task Learning for Multilingual Neural Machine Translation
In this work, we propose a multi-task learning (MTL) framework that jointly trains the model with the translation task on bitext data and two denoising tasks on the monolingual data.
Towards a Soft Faceted Browsing Scheme for Information Access
Faceted browsing is a commonly supported feature of user interfaces for access to information.
Non-local Attention Learning on Large Heterogeneous Information Networks
In this way, it leverages both local and non-local information simultaneously.
Ranked #1 on
Heterogeneous Node Classification
on DBLP (PACT) 14k
(Macro-F1 (60% training data) metric)
Cooperative Reasoning on Knowledge Graph and Corpus: A Multi-agentReinforcement Learning Approach
We model the question answering on KG as a cooperative task between two agents, a knowledge graph reasoning agent and an information extraction agent.
Improving N-gram Language Models with Pre-trained Deep Transformer
Although n-gram language models (LMs) have been outperformed by the state-of-the-art neural LMs, they are still widely used in speech recognition due to its high efficiency in inference.
Learning to Order Sub-questions for Complex Question Answering
Answering complex questions involving multiple entities and relations is a challenging task.
TILM: Neural Language Models with Evolving Topical Influence
Specifically, we propose a novel language model called Topical Influence Language Model (TILM), which is a novel extension of a neural language model to capture the influences on the contents in one text stream by the evolving topics in another related (or possibly same) text stream.
Multi-Agent Dual Learning
Dual learning has attracted much attention in machine learning, computer vision and natural language processing communities.
Ranked #1 on
Machine Translation
on WMT2016 English-German
Adapting Sequence to Sequence models for Text Normalization in Social Media
Social media offer an abundant source of valuable raw data, however informal writing can quickly become a bottleneck for many natural language processing (NLP) tasks.
Ranked #3 on
Lexical Normalization
on LexNorm
JIM: Joint Influence Modeling for Collective Search Behavior
This assumption is unrealistic as there are many correlated events in the real world which influence each other and thus, would pose a joint influence on the user search behavior rather than posing influence independently.
On Application of Learning to Rank for E-Commerce Search
In this paper, we discuss the practical challenges in applying learning to rank methods to E-Com search, including the challenges in feature representation, obtaining reliable relevance judgments, and optimally exploiting multiple user feedback signals such as click rates, add-to-cart ratios, order rates, and revenue.
Non-Autoregressive Machine Translation with Auxiliary Regularization
However, the high efficiency has come at the cost of not capturing the sequential dependency on the target side of translation, which causes NAT to suffer from two kinds of translation errors: 1) repeated translations (due to indistinguishable adjacent decoder hidden states), and 2) incomplete translations (due to incomplete transfer of source side information via the decoder hidden states).
Modeling Diverse Relevance Patterns in Ad-hoc Retrieval
The local matching layer focuses on producing a set of local relevance signals by modeling the semantic matching between a query and each passage of a document.
Semantic Text Analysis for Detection of Compromised Accounts on Social Networks
We propose to use the difference of language models of users and adversaries to define novel interpretable semantic features for measuring semantic incoherence in a message stream.
Identifying Humor in Reviews using Background Text Sources
We study the problem of automatically identifying humorous text from a new kind of text data, i. e., online reviews.
High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm
We apply our generic algorithm to two illustrative latent variable models: Gaussian mixture model and mixture of linear regression, and demonstrate the advantages of our algorithm by both theoretical analysis and numerical experiments.
