Search Results for author:
Found 34 papers, 18 papers with code
OPERA: Operation-Pivoted Discrete Reasoning over Text
To inherit the advantages of these two types of methods, we propose OPERA, an operation-pivoted discrete reasoning framework, where lightweight symbolic operations (compared with logical forms) as neural modules are utilized to facilitate the reasoning ability and interpretability.
Don’t Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Such training objective is sub-optimal when the target sequence is not perfect, e. g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.
Interactive-KBQA: Multi-Turn Interactions for Knowledge Base Question Answering with Large Language Models
For each category of complex question, we devised exemplars to guide LLMs through the reasoning processes.
HopPG: Self-Iterative Program Generation for Multi-Hop Question Answering over Heterogeneous Knowledge
To alleviate these challenges, we propose a self-iterative framework for multi-hop program generation (HopPG) over heterogeneous knowledge, which leverages the previous execution results to retrieve supporting facts and generate subsequent programs hop by hop.
AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets
High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design.
SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation
The pre-trained model can capture enriched visual concepts for images by learning from a large scale of text-image data.
Ranked #1 on
Semantic Segmentation
on PASCAL VOC
MoNET: Tackle State Momentum via Noise-Enhanced Training for Dialogue State Tracking
Then, the noised previous state is used as the input to learn to predict the current state, improving the model's ability to update and correct slot values.
P$^3$LM: Probabilistically Permuted Prophet Language Modeling for Generative Pre-Training
Conventional autoregressive left-to-right (L2R) sequence generation faces two issues during decoding: limited to unidirectional target sequence modeling, and constrained on strong local dependencies.
MuGER$^2$: Multi-Granularity Evidence Retrieval and Reasoning for Hybrid Question Answering
To preserve the advantage and eliminate the disadvantage of different granularity evidence, we propose MuGER$^2$, a Multi-Granularity Evidence Retrieval and Reasoning approach.
Mars: Modeling Context & State Representations with Contrastive Learning for End-to-End Task-Oriented Dialog
Traditional end-to-end task-oriented dialog systems first convert dialog context into belief state and action state before generating the system response.
UniRPG: Unified Discrete Reasoning over Table and Text as Program Generation
Question answering requiring discrete reasoning, e. g., arithmetic computing, comparison, and counting, over knowledge is a challenging task.
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking
The unlabeled data of the DST task is incorporated into the self-training iterations, where the pseudo labels are predicted by a DST model trained on limited labeled data in advance.
Channel Modeling for UAV-to-Ground Communications with Posture Variation and Fuselage Scattering Effect
Unmanned aerial vehicle (UAV)-to-ground (U2G) channel models play a pivotal role for reliable communications between UAV and ground terminal.
A Realistic 3D Non-Stationary Channel Model for UAV-to-Vehicle Communications Incorporating Fuselage Posture
Considering the unmanned aerial vehicle (UAV) three-dimensional (3D) posture, a novel 3D non-stationary geometry-based stochastic model (GBSM) is proposed for multiple-input multiple-output (MIMO) UAV-to-vehicle (U2V) channels.
AutoQGS: Auto-Prompt for Low-Resource Knowledge-based Question Generation from SPARQL
This study investigates the task of knowledge-based question generation (KBQG).
Composable Text Controls in Latent Space with ODEs
This paper proposes a new efficient approach for composable text operations in the compact latent space of text.
Ranked #2 on
Unsupervised Text Style Transfer
on Yelp
BORT: Back and Denoising Reconstruction for End-to-End Task-Oriented Dialog
To enhance the denoising capability of the model to reduce the impact of error propagation, denoising reconstruction is used to reconstruct the corrupted dialog state and response.
LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking
Dialogue state tracking (DST) aims to predict the current dialogue state given the dialogue history.
OPERA:Operation-Pivoted Discrete Reasoning over Text
To inherit the advantages of these two types of methods, we propose OPERA, an operation-pivoted discrete reasoning framework, where lightweight symbolic operations (compared with logical forms) as neural modules are utilized to facilitate the reasoning ability and interpretability.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
Fine- and Coarse-Granularity Hybrid Self-Attention for Efficient BERT
To confront this, we propose FCA, a fine- and coarse-granularity hybrid self-attention that reduces the computation cost through progressively shortening the computational sequence length in self-attention.
CUGE: A Chinese Language Understanding and Generation Evaluation Benchmark
We argue that for general-purpose language intelligence evaluation, the benchmark itself needs to be comprehensive and systematic.
RoR: Read-over-Read for Long Document Machine Reading Comprehension
To address this problem, we propose RoR, a read-over-read method, which expands the reading field from chunk to document.
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension
Reasoning machine reading comprehension (R-MRC) aims to answer complex questions that require discrete reasoning based on text.
CUSTOM: Aspect-Oriented Product Summarization for E-Commerce
To address the problem, we propose CUSTOM, aspect-oriented product summarization for e-commerce, which generates diverse and controllable summaries towards different product aspects.
Don't Take It Literally: An Edit-Invariant Sequence Loss for Text Generation
Such training objective is sub-optimal when the target sequence is not perfect, e. g., when the target sequence is corrupted with noises, or when only weak sequence supervision is available.
RevCore: Review-augmented Conversational Recommendation
Existing conversational recommendation (CR) systems usually suffer from insufficient item information when conducted on short dialogue history and unfamiliar items.
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation
Keyphrases, that concisely summarize the high-level topics discussed in a document, can be categorized into present keyphrase which explicitly appears in the source text, and absent keyphrase which does not match any contiguous subsequence but is highly semantically related to the source.
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training
This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations.
Table-to-Text: Describing Table Region with Natural Language
The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the semantics of a table.
Content-Based Table Retrieval for Web Queries
Understanding the connections between unstructured text and semi-structured table is an important yet neglected problem in natural language processing.
Constraint-Based Question Answering with Knowledge Graph
WebQuestions and SimpleQuestions are two benchmark data-sets commonly used in recent knowledge-based question answering (KBQA) work.
