pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing And Improving Neural Speaker Embeddings for ASR
Authors: Christoph Lüscher, Jingjing Xu, Mohammad Zeineldeen, Ralf Schlüter, Hermann Ney
Date: 2023-01-11 00:00:00
Tasks: Speaker Verification

Neural speaker embeddings encode the speaker's speech characteristics through a DNN model and are prevalent for speaker verification tasks. However, few studies have investigated the usage of neural speaker embeddings for an ASR system. In this work, we present our efforts w.r.t integrating neural speaker embeddings into a conformer based hybrid HMM ASR system. For ASR, our improved embedding extraction pipeline in combination with the Weighted-Simple-Add integration method results in x-vector and c-vector reaching on par performance with i-vectors. We further compare and analyze different speaker embeddings. We present our acoustic model improvements obtained by switching from newbob learning rate schedule to one cycle learning schedule resulting in a ~3% relative WER reduction on Switchboard, additionally reducing the overall training time by 17%. By further adding neural speaker embeddings, we gain additional ~3% relative WER improvement on Hub5'00. Our best Conformer-based hybrid ASR system with speaker embeddings achieves 9.0% WER on Hub5'00 and Hub5'01 with training on SWB 300h.
Few-shot Learning for Cross-Target Stance Detection by Aggregating Multimodal Embeddings
Authors: Parisa Jamadi Khiabani, Arkaitz Zubiaga
Date: 2023-01-11 00:00:00
Tasks: Few-Shot Learning, Stance Detection

Despite the increasing popularity of the stance detection task, existing approaches are predominantly limited to using the textual content of social media posts for the classification, overlooking the social nature of the task. The stance detection task becomes particularly challenging in cross-target classification scenarios, where even in few-shot training settings the model needs to predict the stance towards new targets for which the model has only seen few relevant samples during training. To address the cross-target stance detection in social media by leveraging the social nature of the task, we introduce CT-TN, a novel model that aggregates multimodal embeddings derived from both textual and network features of the data. We conduct experiments in a few-shot cross-target scenario on six different combinations of source-destination target pairs. By comparing CT-TN with state-of-the-art cross-target stance detection models, we demonstrate the effectiveness of our model by achieving average performance improvements ranging from 11% to 21% across different baseline models. Experiments with different numbers of shots show that CT-TN can outperform other models after seeing 300 instances of the destination target. Further, ablation experiments demonstrate the positive contribution of each of the components of CT-TN towards the final performance. We further analyse the network interactions between social media users, which reveal the potential of using social features for cross-target stance detection.
The Role of Interactive Visualization in Explaining (Large) NLP Models: from Data to Inference
Authors: Richard Brath, Daniel Keim, Johannes Knittel, SHimei Pan, Pia Sommerauer, Hendrik Strobelt
Date: 2023-01-11 00:00:00

With a constant increase of learned parameters, modern neural language models become increasingly more powerful. Yet, explaining these complex model's behavior remains a widely unsolved problem. In this paper, we discuss the role interactive visualization can play in explaining NLP models (XNLP). We motivate the use of visualization in relation to target users and common NLP pipelines. We also present several use cases to provide concrete examples on XNLP with visualization. Finally, we point out an extensive list of research opportunities in this field.
Deteksi Depresi dan Kecemasan Pengguna Twitter Menggunakan Bidirectional LSTM
Authors: Kuncahyo Setyo Nugroho, Ismail Akbar, Affi Nizar Suksmawati, Istiadi
Date: 2023-01-11 00:00:00
Tasks: Sentence, text-classification, Text Classification

The most common mental disorders experienced by a person in daily life are depression and anxiety. Social stigma makes people with depression and anxiety neglected by their surroundings. Therefore, they turn to social media like Twitter for support. Detecting users with potential depression and anxiety disorders through textual data is not easy because they do not explicitly discuss their mental state. It takes a model that can identify potential users who experience depression and anxiety on textual data to get treatment earlier. Text classification techniques can achieve this. One approach that can be used is LSTM as an RNN architecture development in dealing with vanishing gradient problems. Standard LSTM does not capture enough information because it can only read sentences from one direction. Meanwhile, Bidirectional LSTM (BiLSTM) is a two-way LSTM that can capture information without ignoring the context and meaning of a sentence. The proposed BiLSTM model is higher than all traditional machine learning models and standard LSTMs. Based on the test results, the highest accuracy obtained by BiLSTM reached 94.12%. This study has succeeded in developing a model for the detection of depression and anxiety in Twitter users.
Multilingual Entity and Relation Extraction from Unified to Language-specific Training
Authors: Zixiang Wang, Jian Yang, Tongliang Li, Jiaheng Liu, Ying Mo, Jiaqi Bai, Longtao He, Zhoujun Li
Date: 2023-01-11 00:00:00
Tasks: Relation, Relation Extraction, Sentence

Entity and relation extraction is a key task in information extraction, where the output can be used for downstream NLP tasks. Existing approaches for entity and relation extraction tasks mainly focus on the English corpora and ignore other languages. Thus, it is critical to improving performance in a multilingual setting. Meanwhile, multilingual training is usually used to boost cross-lingual performance by transferring knowledge from languages (e.g., high-resource) to other (e.g., low-resource) languages. However, language interference usually exists in multilingual tasks as the model parameters are shared among all languages. In this paper, we propose a two-stage multilingual training method and a joint model called Multilingual Entity and Relation Extraction framework (mERE) to mitigate language interference across languages. Specifically, we randomly concatenate sentences in different languages to train a Language-universal Aggregator (LA), which narrows the distance of embedding representations by obtaining the unified language representation. Then, we separate parameters to mitigate interference via tuning a Language-specific Switcher (LS), which includes several independent sub-modules to refine the language-specific feature representation. After that, to enhance the relational triple extraction, the sentence representations concatenated with the relation feature are used to recognize the entities. Extensive experimental results show that our method outperforms both the monolingual and multilingual baseline methods. Besides, we also perform detailed analysis to show that mERE is lightweight but effective on relational triple extraction and mERE{} is easy to transfer to other backbone models of multi-field tasks, which further demonstrates the effectiveness of our method.
Counteracts: Testing Stereotypical Representation in Pre-trained Language Models
Authors: Damin Zhang, Julia Rayz, Romila Pradhan
Date: 2023-01-11 00:00:00
Tasks: Natural Language Understanding

Recently, language models have demonstrated strong performance on various natural language understanding tasks. Language models trained on large human-generated corpus encode not only a significant amount of human knowledge, but also the human stereotype. As more and more downstream tasks have integrated language models as part of the pipeline, it is necessary to understand the internal stereotypical representation in order to design the methods for mitigating the negative effects. In this paper, we use counterexamples to examine the internal stereotypical knowledge in pre-trained language models (PLMs) that can lead to stereotypical preference. We mainly focus on gender stereotypes, but the method can be extended to other types of stereotype. We evaluate 7 PLMs on 9 types of cloze-style prompt with different information and base knowledge. The results indicate that PLMs show a certain amount of robustness against unrelated information and preference of shallow linguistic cues, such as word position and syntactic structure, but a lack of interpreting information by meaning. Such findings shed light on how to interact with PLMs in a neutral approach for both finetuning and evaluation.
Topics in Contextualised Attention Embeddings
Authors: Mozhgan Talebpour, Alba Garcia Seco de Herrera, Shoaib Jameel
Date: 2023-01-11 00:00:00
Tasks: Clustering, Language Modeling, Language Modelling, Topic Models

Contextualised word vectors obtained via pre-trained language models encode a variety of knowledge that has already been exploited in applications. Complementary to these language models are probabilistic topic models that learn thematic patterns from the text. Recent work has demonstrated that conducting clustering on the word-level contextual representations from a language model emulates word clusters that are discovered in latent topics of words from Latent Dirichlet Allocation. The important question is how such topical word clusters are automatically formed, through clustering, in the language model when it has not been explicitly designed to model latent topics. To address this question, we design different probe experiments. Using BERT and DistilBERT, we find that the attention framework plays a key role in modelling such word topic clusters. We strongly believe that our work paves way for further research into the relationships between probabilistic topic models and pre-trained language models.
Word-Graph2vec: An efficient word embedding approach on word co-occurrence graph using random walk technique
Authors: Wenting Li, Jiahong Xue, Xi Zhang, Huacan Chen, Zeyu Chen, Feijuan Huang, Yuanzhe Cai
Date: 2023-01-11 00:00:00
Tasks: Information Retrieval, Machine Translation, Retrieval

Word embedding has become ubiquitous and is widely used in various natural language processing (NLP) tasks, such as web retrieval, web semantic analysis, and machine translation, and so on. Unfortunately, training the word embedding in a relatively large corpus is prohibitively expensive. We propose a graph-based word embedding algorithm, called Word-Graph2vec, which converts the large corpus into a word co-occurrence graph, then takes the word sequence samples from this graph by randomly traveling and trains the word embedding on this sampling corpus in the end. We posit that because of the limited vocabulary, huge idioms, and fixed expressions in English, the size and density of the word co-occurrence graph change slightly with the increase in the training corpus. So that Word-Graph2vec has stable runtime on the large-scale data set, and its performance advantage becomes more and more obvious with the growth of the training corpus. Extensive experiments conducted on real-world datasets show that the proposed algorithm outperforms traditional Word2vec four to five times in terms of efficiency and two to three times than FastText, while the error generated by the random walk technique is small.
MGeo: Multi-Modal Geographic Pre-Training Method
Authors: Ruixue Ding, Boli Chen, Pengjun Xie, Fei Huang, Xin Li, Qiang Zhang, Yao Xu
Date: 2023-01-11 00:00:00
Tasks: Language Modelling

As a core task in location-based services (LBS) (e.g., navigation maps), query and point of interest (POI) matching connects users' intent with real-world geographic information. Recently, pre-trained models (PTMs) have made advancements in many natural language processing (NLP) tasks. Generic text-based PTMs do not have enough geographic knowledge for query-POI matching. To overcome this limitation, related literature attempts to employ domain-adaptive pre-training based on geo-related corpus. However, a query generally contains mentions of multiple geographic objects, such as nearby roads and regions of interest (ROIs). The geographic context (GC), i.e., these diverse geographic objects and their relationships, is therefore pivotal to retrieving the most relevant POI. Single-modal PTMs can barely make use of the important GC and therefore have limited performance. In this work, we propose a novel query-POI matching method Multi-modal Geographic language model (MGeo), which comprises a geographic encoder and a multi-modal interaction module. MGeo represents GC as a new modality and is able to fully extract multi-modal correlations for accurate query-POI matching. Besides, there is no publicly available benchmark for this topic. In order to facilitate further research, we build a new open-source large-scale benchmark Geographic TExtual Similarity (GeoTES). The POIs come from an open-source geographic information system (GIS). The queries are manually generated by annotators to prevent privacy issues. Compared with several strong baselines, the extensive experiment results and detailed ablation analyses on GeoTES demonstrate that our proposed multi-modal pre-training method can significantly improve the query-POI matching capability of generic PTMs, even when the queries' GC is not provided. Our code and dataset are publicly available at https://github.com/PhantomGrapes/MGeo.
User-Centered Security in Natural Language Processing
Authors: Chris Emmery
Date: 2023-01-10 00:00:00
Tasks: Author Profiling, Privacy Preserving

This dissertation proposes a framework of user-centered security in Natural Language Processing (NLP), and demonstrates how it can improve the accessibility of related research. Accordingly, it focuses on two security domains within NLP with great public interest. First, that of author profiling, which can be employed to compromise online privacy through invasive inferences. Without access and detailed insight into these models' predictions, there is no reasonable heuristic by which Internet users might defend themselves from such inferences. Secondly, that of cyberbullying detection, which by default presupposes a centralized implementation; i.e., content moderation across social platforms. As access to appropriate data is restricted, and the nature of the task rapidly evolves (both through lexical variation, and cultural shifts), the effectiveness of its classifiers is greatly diminished and thereby often misrepresented. Under the proposed framework, we predominantly investigate the use of adversarial attacks on language; i.e., changing a given input (generating adversarial samples) such that a given model does not function as intended. These attacks form a common thread between our user-centered security problems; they are highly relevant for privacy-preserving obfuscation methods against author profiling, and adversarial samples might also prove useful to assess the influence of lexical variation and augmentation on cyberbullying detection.
Memory Augmented Large Language Models are Computationally Universal
Authors: Dale Schuurmans
Date: 2023-01-10 00:00:00
Tasks: Language Modeling, Language Modelling, Large Language Model

We show that transformer-based large language models are computationally universal when augmented with an external memory. Any deterministic language model that conditions on strings of bounded length is equivalent to a finite automaton, hence computationally limited. However, augmenting such models with a read-write memory creates the possibility of processing arbitrarily large inputs and, potentially, simulating any algorithm. We establish that an existing large language model, Flan-U-PaLM 540B, can be combined with an associative read-write memory to exactly simulate the execution of a universal Turing machine, $U_{15,2}$. A key aspect of the finding is that it does not require any modification of the language model weights. Instead, the construction relies solely on designing a form of stored instruction computer that can subsequently be programmed with a specific set of prompts.
CoSPLADE: Contextualizing SPLADE for Conversational Information Retrieval
Authors: Nam Le Hai, Thomas Gerald, Thibault Formal, Jian-Yun Nie, Benjamin Piwowarski, Laure Soulier
Date: 2023-01-11 00:00:00
Tasks: Conversational Search, Information Retrieval, Learning-To-Rank, Retrieval

Conversational search is a difficult task as it aims at retrieving documents based not only on the current user query but also on the full conversation history. Most of the previous methods have focused on a multi-stage ranking approach relying on query reformulation, a critical intermediate step that might lead to a sub-optimal retrieval. Other approaches have tried to use a fully neural IR first-stage, but are either zero-shot or rely on full learning-to-rank based on a dataset with pseudo-labels. In this work, leveraging the CANARD dataset, we propose an innovative lightweight learning technique to train a first-stage ranker based on SPLADE. By relying on SPLADE sparse representations, we show that, when combined with a second-stage ranker based on T5Mono, the results are competitive on the TREC CAsT 2020 and 2021 tracks.
Collision-free Source Seeking and Flocking Control of Multi-agents with Connectivity Preservation
Authors: Tinghua Li, Bayu Jayawardhana
Date: 2023-01-11 00:00:00
Tasks: Collision Avoidance

We present a distributed source-seeking and flocking control method for networked multi-agent systems with non-holonomic constraints. Based solely on identical on-board sensor systems, which measure the source local field, the group objective is attained by appointing a leader agent to seek the source while the remaining follower agents form a cohesive flocking with their neighbors using a distributed flocking control law in a connectivity-preserved undirected network. To guarantee the safe separation and group motion for all agents and to solve the conflicts with the "cohesion" flocking rule of Reynolds, the distributed control algorithm is solved individually through quadratic-programming optimization problem with constraints, which guarantees the inter-agent collision avoidance and connectivity preservation. Stability analysis of the closed-loop system is presented and the efficacy of the methods is shown in simulation results.
Trajectory tracking control of the second-order chained form system by using state transitions
Authors: Mayu Nakayama, Masahide Ito
Date: 2023-01-11 00:00:00
Tasks: Form

This paper proposes a novel control approach composed of sinusoidal reference trajectories and trajectory tracking controller for the second-order chained form system. The system is well-known as a canonical form for a class of second-order nonholonomic systems obtained by appropriate transformation of the generalized coordinates and control inputs. The system is decomposed into three subsystems, two of them are the so-called double integrators and the other subsystem is a nonlinear system depending on one of the double integrators. The double integrators are linearly controllable, which enables to transit the value of the position state in order to modify the nature of the nonlinear system that depends on them. Transiting the value to "one" corresponds to modifying the nonlinear subsystem into the double integrator; transiting the value to "zero" corresponds to modifying the nonlinear subsystem into an uncontrollable linear autonomous system. Focusing on this nature, this paper proposes a feedforward control strategy. Furthermore, from the perspective of practical usefulness, the control strategy is extended into trajectory tracking control by using proportional-derivative feedback. The effectiveness of the proposed method is demonstrated through several numerical experiments including an application to an underactuated manipulator.
AdaptSLAM: Edge-Assisted Adaptive SLAM with Resource Constraints via Uncertainty Minimization
Authors: Ying Chen, Hazer Inaltekin, Maria Gorlatova
Date: 2023-01-11 00:00:00
Tasks: Edge-computing, Simultaneous Localization and Mapping

Edge computing is increasingly proposed as a solution for reducing resource consumption of mobile devices running simultaneous localization and mapping (SLAM) algorithms, with most edge-assisted SLAM systems assuming the communication resources between the mobile device and the edge server to be unlimited, or relying on heuristics to choose the information to be transmitted to the edge. This paper presents AdaptSLAM, an edge-assisted visual (V) and visual-inertial (VI) SLAM system that adapts to the available communication and computation resources, based on a theoretically grounded method we developed to select the subset of keyframes (the representative frames) for constructing the best local and global maps in the mobile device and the edge server under resource constraints. We implemented AdaptSLAM to work with the state-of-the-art open-source V- and VI-SLAM ORB-SLAM3 framework, and demonstrated that, under constrained network bandwidth, AdaptSLAM reduces the tracking error by 62% compared to the best baseline method.
Balancing a Stick with Eyes Shut: Inverted Pendulum on a Cart without Angle Measurement
Authors: Bidhayak Goswami, Anindya Chatterjee
Date: 2023-01-11 00:00:00
Tasks: Position, Sensitivity

We consider linear time-invariant dynamic systems in the single-input, single-output (SISO) framework. In particular, we consider stabilization of an inverted pendulum on a cart using a force on the cart. This system is easy to stabilize with pendulum angle feedback. However, with cart position feedback it cannot be stabilized with stable and proper compensators. Here we demonstrate that with an additional compensator in a parallel feedforward loop, stabilization is possible with such compensators. Sensitivity to noise seems to be about 3 times worse than for the situation with angle feedback. For completeness, discussion is presented of compensator parameter choices, robustness, fragility and comparison with another control approach.
TERRA: Beam Management for Outdoor mm-Wave Networks
Authors: Santosh Ganji, Jaewon Kim, Romil Sonigra, P. R. Kumar
Date: 2023-01-10 00:00:00
Tasks: Management

mm-Wave communication systems use narrow directional beams due to the spectrum's characteristic nature: high path and penetration losses. The mobile and the base station primarily employ beams in line of sight (LoS) direction and when needed in non-line of sight direction. Beam management protocol adapts the base station and mobile side beam direction during user mobility and to sustain the link during blockages. To avoid outage in transient pedestrian blockage of the LoS path, the mobile uses reflected or NLoS path available in indoor environments. Reflected paths can sustain time synchronization and maintain connectivity during temporary blockages. In outdoor environments, such reflections may not be available and prior work relied on dense base station deployment or co-ordinated multi-point access to address outage problem. Instead of dense and hence cost-intensive network deployments, we found experimentally that the mobile can capitalize on ground reflection. We developed TERRA protocol to effectively handle mobile side beam direction during transient blockage events. TERRA avoids outage during pedestrian blockages 84.5 $\%$ of the time in outdoor environments on concrete and gravel surfaces. TERRA also enables the mobile to perform a soft handover to a reserve neighbor base station in the event of a permanent blockage, without requiring any side information, unlike the existing works. Evaluations show that TERRA maintains received signal strength close to the optimal solution while keeping track of the neighbor base station.